Exported and imported value in period 2006–2015 (US dollar thousand).
\r\n\t a multi-pronged approach. The pervasive computing paradigm is at a crossroads where never before computing
\r\n\t has been so much embedded within the user. Recent developments in sensor technologies, wireless protocols
\r\n\tintegration, and AI have empowered the citizen towards a smart citizen with a high degree of autonomy and varying
\r\n\tcomputing capabilities from one context to another.
\r\n\t
\r\n\tMoreover, software engineering has evolved too to allow lightweight programming and full-stack coding of those sensors. The network itself is today viewed as a programming platform, thus wearable devices are no more stand-alone and do not operate in a vacuum. This book aims at attracting authors from academia, the industry, research institutions, public and private agencies to provide the findings of their recent achievements in the field, but also visionaries who foresee the future of wearable technologies in the coming decades.
Honeybees play a crucial role for the well-being of the humanity: on the one hand, they have the amazing capacity to produce honey and other secondary products (pollen, propolis, royal jelly, venom and wax) and on the other hand, they provide pollination services for plants, bringing an important contribution to the agricultural activities and food production. The bees represent a bio barometer for the preservation of the environment. In addition, their economic, cultural and religious values are very important [1].
\nThe European Union is one of the most important honey producers of the world. Nevertheless, according to some official bodies, production levels seem to be declining [2]. Such decrease is attributable to the diminution of honeybee colonies, which is mostly related to the colony collapse disorder, but also to imports. The number of beehives, the quantity and quality of honey obtained are the main aspects evaluated at European level, to measure the impact of supporting policies.
\nBeekeeping—as agricultural activity—could be considered as multifunctional, because it performs several functions that contribute to society’s welfare. At macro-level, beekeeping may play an essential economic role, creating a competitive advantage through innovation and improving the balance trade. This may be particularly true in some countries, rather than in others; however, even in those areas where the contribution of honeybee products to value added and trade of food product is limited, they represent an interesting alternative or complemental entrepreneurial activity. At micro-level, in fact, beekeeping creates well-being for communities, providing health products for population and decent revenues for farmers [1]. It also supports the sustainability of rural livelihoods, as, given the relatively low investment requirements, it is more easily engaged, compared to other rural and agricultural activities.
\nThe key functions of beekeeping are: food security, environmental function, economical function and socio-cultural function. Beekeeping contributes to ensuring food security when people have physical and economic access to honey and other honeybee products or to other typical dishes that use honey as ingredient. The availability of honeybee products depends on local/national production and the capacity of import. The access to honeybee products depends on purchasing power of consumers and proximity to the markets. The food security has two main components: the quantity and the quality.
\nThe quality of honey is one of the main aspects related to food security function of beekeeping. Three main components are usually taken into account in the case of honey quality: the organoleptic properties, the physical and chemical properties and the hygienic aspects (the latter is usually defined “food safety”).
\nThe organoleptic properties—aspect, consistency, colour, taste and smell are evaluated using the sensory analysis. This scientific method is used to establish the botanical origin of honey and its authenticity, but also to classify and define product standards. In addition, the method helps to identify the consumer preferences for different types of honey [3]. In EU countries, the evaluation of physical and chemical parameters of honey is made according to international legislation (Codex Alimentarius Standard), to European Union Directives and to National Legislation [4]. These parameters characterise the naturalness, maturity and unaffectedness of honey [5].
\nUnfortunately, the quality control of honey on the international market is sometimes vulnerable. According to Strayer et al. [6], the adulteration of honey could be economically motivated and influenced by several factors such as: the decrease of domestic production, lack of identity standards, scarce of analytical methods and trade policies. In the context of global market, there is a stringent need for finding solutions to limit the repercussions of the unfair practices on producers and consumers. The identification and characterisation of different types of honey via quality schemes and logos represent a protective solution that creates new opportunities for producers and consumers.
\nEven if compositional, sensory and safety characteristics of food are essential elements of food quality, they do not necessarily exhaust this feature, that goes beyond the observable characteristics of a product. In the last decades, the concept of quality in food (this applies also to honey) has enlarged from intrinsic attributes to extrinsic ones, focussing on some features of the production process such as its social and environmental impacts, animal welfare issues and the link of the food with a certain agricultural area. The latter aspect has received particular emphasis and attention in European countries. The quality recognition of honey (as for all food products) at European Union level can be achieved by obtaining two designations: Protected Designation of Origin and Protected Geographical Indication) [7]. According to Bertozzi [8] the use of geographical name for an agricultural product date from ancient times, “honey from Sicily” being a good example in this sense.
\nProtected Designation of Origin and Protected Geographical Indication tools, created within the Common Agricultural Policy, help beekeepers (and all farmers) to improve the marketing of honeybee products and to guarantee their authenticity and reputation. In the same time, they help consumer to have more trust they are buying high quality products that are connected to special places. Authenticity and traceability are the main aspects in the case of Protected Designation of Origin/Protected Geographical Indication recognition process [9]. The honey authenticity is linked to the specificity of the geographical area where it is produced: environmental factors (climate, soil and flora) and human factors (beekeeping knowledge and skills, traditional/innovative practices). There are several European countries where protected honeys with Protected Designation of Origin/Protected Geographical Indication status could be found: Greece, Spain, France, Italy, Luxembourg, Malta, Poland, Portugal and Ukraine.
\nHoney produced in Serbia, Romania and Italy reflects the rich diversity of melliferous plants but also the particular characteristics of regions. The quality recognition of honey is a volunteer system for beekeepers that allow valorising regional honey at European Union level. Obtaining Protected Designation of Origin and Protected Geographical Indication designation is made in compliance with European Union legislation.
\nIn Italy, there are three types of honey with Protected Designation of Origin certification [10]: “Miele della Lunigiana”, registered from 2004 [11], “Miele delle Dolomiti Bellunesi” registered from 2011 [12] and “Miele Varesino”, registered, in 2014 [13]. “Miele della Lunigiana” belongs to Toscana Region (central Italy) and it is reserved for two types of honey: acacia honey and chestnut honey (one of the healthiest honeys due to its mineral content, antioxidant and antimicrobial properties). “Miele delle Dolomiti Bellunesi” is produced in the mountains of Belluno (in Veneto Region, North-East Italy) and could be found for various types: wildflower, acacia, lime, chestnut, rhododendron and dandelion. It is very appreciated not only for the floral variety, but also for other qualities such as purity, wholesomeness and lengthy shelf life. “Miele Varesino” is an acacia honey from the province of Varese (Lombardy Region, North-West Italy) with a high level of purity, due to the quality of acacia trees that grows widely in this region.
\nFor the moment, in Serbia and Romania, there is no Protected Designation of Origin/Protected Geographical Indication certified honey, but there is a huge potential for developing this protection in the future. In the case of Romania, the Ministry of Agriculture and Rural Development encourages the Protected Designation of Origin/Protected Geographical Indication honey certification and support this process by using the following arguments: the increase of value added of Romanian types of honey, the rise of consumer trust in the reliability of beekeepers who take care of the quality of honey, the creation of a balance between supply and demand by maintaining the quality, the facilitation of traceability and controls and the acceleration to attracting European Union funds [14]. Such strategies implemented by the Ministry of Agriculture and Rural Development could significantly contribute to the development of beekeeping sector.
\nIn the case of Serbia, there is also a huge potential for Protected Designation of Origin/Protected Geographical Indication certification of honey and other food products. An illustrative example is “Vlasina honey”. A study about the attitudes of Vlasina honey producers towards geographical indications reveals that a small part of them know about this certification system, the results indicating the need for education and information in order to familiarise beekeepers with the procedure and the advantages of Protected Designation of Origin/Protected Geographical Indication system [15]. “Vlasina honey” is on the list of local products supported by the European Union and the Government of Switzerland to receive technical assistance for the certification procedure. The specificity of “Vlasina honey” is given by the exceptional qualities of the region: the variety of medicinal plants and the clean environment. This type of honey is unique due to its flavour and therapeutic properties. The European recognition of “Vlasina honey” will increase the competitiveness of the beekeepers’ association “Matica” and will open the opportunity to sell on international markets [16].
\nAt present, a very small quantity of honey produced in Serbia, Romania and Italy is Protected Designation of Origin/Protected Geographical Indication protected. Hence, a question arises: what other tools could be developed to measure the quality of honey produced in these three countries? The price of different types of honey could be a real barometer for evaluating the quality? Or the high demand for export of local honey demonstrates its value?
\nProviding a good quantitative proxy able to describe honey quality is a hard task, as such concept and perception is heterogeneous across consumers. The widely used index to approach quality attributes of a food product is its unit value (price). It is worth remembering that price differences across products may be influenced (along with preferences for quality) by other factors, for instance, production costs and disposable income of consumers; nevertheless, price remains the most available datum that may be related to product quality, even if such correlation may be variable. In the case of honey, its quality is strictly related to product differentiation: the availability of different kind of honeys enlarges the choice set of consumers, increasing their satisfaction.
\nThe above-mentioned considerations on honey would suggest analysing and comparing price trends for a set of different kind of honey, over time and across the three countries examined. Such a comparison would allow grasping some insights on the relative quality of each honey examined, assuming some price-quality relationship. Unfortunately, this strategy cannot be followed, mainly for a matter of data availability on comparability across countries and over time: price data on differentiated honey typology are rarely accessible and even when present, they are usually not gathered by official statistical bodies and are discontinuous over time. It is then clear that for any attempt to renders the concept of quality are necessary data continuous over time that are gathered and processed with homogeneous criteria over countries.
\nSuch characteristics are fully satisfied by trade data, which are available at a high level of detail. Unfortunately, the maximum level of disaggregation for which data on traded products are released refers to “natural honey”, without any further specification about the typology or characteristic of that food item. Even if the lack of information on product differentiation represents a limitation in examining quality differences among honey typologies in each country, using trade data has many advantages.
\nSuch positive aspects are mainly due to both the opportunity to observe trade movements knowing both the value of honey traded (imported and exported) and its quantity. From this information we can derive the unit values (prices) of exchanged honey. Knowing the volume of trade, along with average import and export prices is highly valuable information as it allows analysing trade flows using a set of indexes. Such indexes, developed within the traditional trade theory of comparative advantage, tell us, among others, to what extent the honey sector in each country is competitive in its export performances, compared to the whole export of the same country. Also this trade index, along with export and import prices, may be an indirect measure of quality of honey exchanged by the selected countries.
\nIt is quite intuitive that the ability of a product (honey) to be demanded beyond its domestic market, overcoming trade cost and cultural barriers may be seen as a combination of factors like its perceived quality that meets preferences of foreign consumers. For the same reason measures of competitiveness in trade are related on one hand to honey quality and on the other to the efficiency of beekeepers (and of their bees) to yield a product that satisfy consumers beyond the domestic market. For this reason the rest of the chapter is focussed on such topic, with the twofold objective to provide a description, even though indirect, of both the quality of the honey traded and the competitiveness of beekeepers and honey sector in the selected countries (Serbia, Romania and Italy).
\nThe research subject is the analysis of production and trade of honey in three European Union countries: Romania, Italy and Serbia, with a goal to consider the dynamic of supply and trade of honey and point out the problem faced by producers. The goal of the study is to give a practical implication to the results obtained, by proposing concrete measures to improve the existing situation.
In the following sections, we analysed the level and growth rate of honey production. An entire set of trade indicators related to honey were dynamically presented in the paper: value, amount, growth rate and geographic structure of export and import in the world, European Union and selected countries. The authors also measured the level of comparative advantages of exported honey from the selected countries by using the Balassa index. Research included a 10-year period. For this purpose, there were used data from Faostat, UN Comtrade and ITC (0409 product code), but also data provided by National Statistics Bodies.
\nThe main body of our analysis deals with computation and comparison of the honey sector competitiveness in Serbia, Romania and Italy, to measure the comparative advantage of the honey export. The existence and extent of correlations among trade indexes is also performed. The basic concept of comparative advantage was erected in 1965 and the original Balassa model is given in Ref. [17]:
\nwhere Xij is export of product j (honey in this case) from countries (Serbia, Romania and Italy, in this analysis); Xit is total export of Serbia, Romania and Italy; Xnj is total export of honey from world and Xnt is total export of the world. For values B > 1, the comparative advantage in honey export of the country examined is revealed. In other words, there is comparative advantage in honey export by the country when the share of honey exported on total export of the country (Xij/Xit) is bigger than the share of honey world export on total world export (Xnj/Xnt). Ref. [18] made the correction of the index of comparative advantage and he presented it as relative trade advantage (RTA). Relative trade advantage (RTA) stands for the difference between the relative advantages of export (RXA) and the relative merits of import (RMA).
\nwhere Mij is import of honey from Serbia, Romania and Italy, Mit is total import from Serbia, Romania and Italy, Mnj is total import of honey from the world and Mnt is total import from world. The interpretation of the relative import advantage index is symmetrical with respect to the relative advantages of export (or B) Index: the country examined is relatively more “vulnerable” to honey import (compared to its entire economy) when the share of honey imported on total import of the country (Mij/Mit) is bigger than the share of honey world import on total world import (Mnj/Mnt). Calculating more accurate comparative advantages, Ref. [18] has created another index as the natural logarithm (ln) of the relative advantages of exports and imports (ln RXA and ln RMA). The difference obtained between the relative advantages of exports and imports is the revealed competitiveness (RC) and is expressed as:
\nFrom the above-mentioned formula, Refs. [19, 20], has developed the following, to calculate the explicit comparative advantage:
\nwhere X is the value of export, M is value of import, index i presents honey sector.
Results of the research show that within the analysed period the value of exports and imports increased on both the global level and in the analysed countries (Table 1). Comparing the change in import and export along the time span we have used the average annual growth rate (g), computed as:
Exporters | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | Average | Growth rate (%) |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Exported value | ||||||||||||
World | 830,290 | 894,356 | 1294,224 | 1252,091 | 1488,906 | 1701,343 | 1768,323 | 2076,365 | 2329,733 | 2355,486 | 1599,111.7 | 12.3 |
Romania | 20,593 | 16,322 | 24,933 | 41,802 | 41,953 | 41,230 | 44,593 | 54,572 | 53,919 | 46,020 | 38,593.7 | 9.3 |
Italy | 12,339 | 16,038 | 19,478 | 17,674 | 31,236 | 32,639 | 38,392 | 59,117 | 46,385 | 43,800 | 31,709.8 | 15.1 |
Serbia | 537 | 1221 | 2116 | 3220 | 7537 | 5120 | 12,908 | 14,881 | 8690 | 9670 | 6590 | 37.9 |
Importers | Imported value | Average | Growth rate (%) | |||||||||
World | 828,801 | 921,846 | 1251,716 | 1281,993 | 1508,615 | 1717,049 | 1760,692 | 2037,321 | 2325,266 | 2327,362 | 1596,066.1 | 12.2 |
Italy | 28,305 | 25,098 | 44,864 | 51,967 | 53,363 | 57,967 | 56,116 | 75,188 | 91,183 | 84,534 | 56,858.5 | 12.9 |
Romania | 159 | 775 | 2006 | 1545 | 2383 | 3656 | 5144 | 5546 | 8302 | 6183 | 3569.9 | 50.2 |
Serbia | 24 | 45 | 21 | 59 | 158 | 74 | 9 | 240 | 10 | 120 | 76 | 19.6 |
Exported and imported value in period 2006–2015 (US dollar thousand).
where f is the final value of the series (year 2015), i is the initial value of the series (2006) and n is the time length (9 years). The interpretation of this formula is: a 5% of average growth rate means that, starting from the initial value (at 2006) i, it is necessary an annual increase of 5% to obtain the final value (at 2015) f. Average annual export growth rates in value show that Serbia had the highest average growth—37.9%. However, even with such a high value, Serbia did not achieve significant results in absolute terms, so that the average annual export amounted to USD 6.5 million with considerable oscillations per years. Romania had the highest average value of export amounting to USD 38.6 million, although over the last few years, exports increased considerably, at 9% per year, on average. The value of honey exports from Italy was growing at the rate higher than Romania but far lower than Serbia (15.1% per year) with the average value of exports being USD 31.7 million. Import data, in value, shows that Romania had the highest average annual growth rate—50.2% with average annual imports of USD 3.6 million that is however lower than the export value, with positive trade balance (export-import) of USD 35 million, on average. This makes Romania a net exporter of honey in value. Serbia imported certain quantities of honey in some years; however, they were insignificant, amounting to the average of USD 76,000. Also Serbia is a net exporter with a positive trade balance of USD 65,000, on average. Italy had the highest average value of imports amounting to USD 56.9 million with a considerable increase in imports over the last few years. Unlike Romania and Serbia, Italy is a net importer, in value, of honey with the average (2006–2015) value of import exceeding by UDS 25 million the export.
\nSwitching from values to quantity traded (Table 2), the research show that over 2006–2015 period the quantity of exports and imports increased on both the global level and in the selected countries. In terms of export and import quantities, Serbia had the highest average annual growth rate—32.8%. Note that, as export quantity has grown less than export value, the unit value of exported Serbian honey grown, in nominal terms, over that period. However, the average annual quantity of honey exported from Serbia falls considerably behind Romania and Italy amounting to 1530 tons. Romania had the highest average value of exports amounting to 10,000 tons. Within the analysed period, only small oscillations in exported quantities are shown, which points to the fact that production and supply on international market were balanced. The quantity of honey exports from Italy was growing at the rate of 10.3% per year with average quantity of exports amounting to 6544 tons.
Exporters | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | Average | Growth rate (%) |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Exported quantity tons | ||||||||||||
World | 427,991 | 393,733 | 471,037 | 412,160 | 472,577 | 492,269 | N/A | 585,045 | 623,878 | 667,599 | 505,143.2 | 5.1 |
Romania | 9606 | 6255 | 7087 | 10,654 | 11,017 | 9899 | 11,542 | 12,649 | 11,116 | 10,863 | 10,068.8 | 1.4 |
Italy | 3669 | 3994 | 4082 | 3454 | 6960 | 6444 | 8352 | 11,507 | 8093 | 8882 | 6543.7 | 10.3 |
Serbia | 159 | 390 | 638 | 913 | 1916 | 1108 | 2966 | 3368 | 1804 | 2045 | 1530.7 | 32.8 |
Importers | Imported quantity | Average | Growth rate (%) | |||||||||
World | 439,322 | 422,411 | 452,659 | 439,810 | 499,914 | 501,863 | 531,285 | 583,653 | 625,577 | 646,299 | 514,279.3 | 4.4 |
Italy | 13,785 | 10,781 | 13,584 | 15,261 | 14,560 | 15,152 | 15,220 | 18,493 | 21,174 | 23,549 | 16,155.9 | 6.1 |
Romania | 63 | 315 | 777 | 516 | 880 | 1067 | 1824 | 2967 | 2577 | 2450 | 1343.6 | 50.2 |
Serbia | 8 | 16 | 3 | 9 | 43 | 15 | 1 | 61 | 0 | 15 | 17.1 | 7.2 |
Exported and imported quantity in period 2006–2015 (tons).
Average annual growth rates of the quantity of honey imports show that Romania had the highest value—50.2% with average annual imports of 1343 tons. Nevertheless, the quantity exported from Romania is higher of 8725 tons than the imported (80% of total Romanian trade of honey). Serbia imported certain quantities of honey in some years, however, they are quite insignificant amounting to the average of 17.7 tons and confirming that Serbia is a net exporter of honey. Italy had the highest average quantity of imports amounting to 16,200 tons, far bigger than its export quantity of 9600 tons, making evident that Italy is a net importer of honey.
\nTable 3 emerges in the analysed period that there was an increase in the average price of honey at the rate of 7% per year on the global level. Within such period, Italy reached, on average, the highest export price of honey amounting to USD 4722 per ton, with considerable increase over the last few years. Serbia was exporting honey at the average price of USD 4023 per ton and had very low growth rate within the analysed period. Romania had the lowest average export price of honey amounting to USD 3746 with an average growth rate of 7.9% per year. At the end of the first section, we discussed on the relation between quality and price, suggesting that the former may be somehow reflected in the latter. Even if this concept is reasonable and commonly accepted, this may not apply when comparing prices across countries. In other words, the three-time series of unit value of exported honey are not comparable, that in turn means that higher export prices from a country does not necessarily imply higher quality. Even if quality is a component of the export price this may be also strongly affected by inflationary dynamics and by disposable income of the partners (importers) countries where honey is exported; also transport and other trade costs may play a role in determining export price. For these reasons, time series of exported honey unit values may be interesting if compared, for the same country, over time (and not across countries).
Exporters | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | Average | Growth rate (%) |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Exported unit value US dollar/tons | ||||||||||||
World | 1940 | 2271 | 2748 | 3038 | 3151 | 3456 | N/A | 3543 | 3782 | 3565 | 3054.89 | 7.0 |
Romania | 2144 | 2609 | 3518 | 3924 | 3808 | 4165 | 3891 | 4314 | 4851 | 4236 | 3746.00 | 7.9 |
Italy | 3363 | 4016 | 4772 | 5117 | 4488 | 5065 | 4597 | 5137 | 5731 | 4931 | 4721.70 | 4.3 |
Serbia | 3382 | 3131 | 3317 | 3527 | 3934 | 4621 | 4352 | 4418 | 4817 | 4729 | 4022.80 | 3.8 |
Exported unit value US dollar/tons.
Italy’s exports represent 1.86% of world exports for this product its ranking in world exports is 17 (Table 4). The average distance of importing countries is 1530 km and the export concentration is 0.22. Serbia’s exports represent 0.41% of world exports for this product its ranking in world exports is 32. The average distance of importing countries is 1199 km and the export concentration is 0.17. Romania’s exports represent 1.96% of world exports for this product its ranking in world exports is 15. The average distance of importing countries is 1635 km and the export concentration is 0.26. Italy’s imports represent 3.65% of world imports for this product its ranking in world imports is 7. The average distance of supplying countries is 2577 km and the market concentration is 0.18. Serbia’s imports represent 0.01% of world imports for this product its ranking in world imports is 110. The average distance of supplying countries is 4438 km and the market concentration is 0.34. Romania’s imports represent 0.27% of world imports for this product its ranking in world imports is 37. The average distance of supplying countries is 2096 km and the market concentration is 0.2.
Importers | Exported value 2015 (USD thousand) | Trade balance 2015 (USD thousand) | Share in countries exports (%) | Exported quantity 2015 | Quantity unit | Unit value (USD/unit) | Exported growth in value between 2011 and 2015 (% p.a.) | Exported growth in quantity between 2011 and 2015 (% p.a.) | Exported growth in value between 2014 and 2015 (% p.a.) | Share of partner countries in world imports (%) | Total import growth in value of partner countries between 2011 and 2015 (% p.a.) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Exported by Italy | World | 43,800 | −40,734 | 100 | 8882 | Tons | 4931 | 8 | 6 | −6 | 100 | 9 |
Exported by Serbia | World | 9670 | 9550 | 100 | 2045 | Tons | 4729 | 9 | 8 | 11 | 100 | 9 |
Exported by Romania | World | 46,045 | 39,880 | 100 | 10,863 | Tons | 4239 | 4 | 1 | −15 | 100 | 9 |
Exporters | Imported value 2015 (USD thousand) | Trade balance 2015 (USD thousand) | Share in Italy’s imports (%) | Imported quantity 2015 | Quantity unit | Unit value (USD/unit) | Imported growth in value between 2011 and 2015 (% p.a.) | Imported growth in quantity between 2011 and 2015 (% p.a.) | Imported growth in value between 2014 and 2015 (% p.a.) | Share of partner countries in world exports (%) | Total export growth in value of partner countries between 2011 and 2015 (% p.a.) | |
Imported by Italy | World | 84,534 | −40,734 | 100 | 23,549 | Tons | 3590 | 13 | 13 | −7 | 100 | 10 |
Imported by Serbia | World | 120 | 9550 | 100 | 15 | Tons | 8000 | 11 | 0 | 1100 | 100 | 10 |
Imported by Romania | World | 6165 | 39,880 | 100 | 2450 | Tons | 2516 | 16 | 22 | −26 | 100 | 10 |
List of importing markets and list of importing markets for the product for the product 0409 natural honey.
Studying comparative advantage in exports of honey from Serbia, we applied five indexes: relative advantages of export, relative import advantage, relative trade advantage, the revealed competitiveness and the Balassa index. Table 5 shows the indexes for all analysed years. The research found a positive comparative advantage of all five indices. Empirical research results of comparative advantage in exports of honey from Serbia in the period 2006–2015 are shown in Table 5.
N | Minimum | Maximum | Mean | Std. deviation | Variance | ||
---|---|---|---|---|---|---|---|
RXA | 10 | 1.20 | 11.93 | 5.2830 | 3.40314 | 11.581 | RXA |
RMA | 10 | 0.00 | .11 | .0420 | .03765 | .001 | RMA |
RTA | 10 | 1.18 | 11.93 | 5.2440 | 3.39224 | 11.507 | RTA |
RC | 10 | 3.80 | 7.78 | 5.0950 | 1.30715 | 1.709 | RC |
RCA | 10 | 1.52 | 4.87 | 2.7480 | 1.11179 | 1.236 | RCA |
Descriptive statistics for RXA, RMA, RTA, RC and RCA indexes of exports of honey from Serbia in the period 2006–2015.
Source: ITC and calculation of the author.
Results of the research of comparative advantage of honey exports from Serbia show that all the five indexes have achieved positive values. The highest level has been achieved with relative export advantage, which has caused a high positive relative trade advantage. Relative import advantage has very low, however positive value and points to negative trends and the presence of some quantities in imports.
\nBy analysing the variance (Table 6), we wanted to determine whether the mean variables vary in relation to the group. In Serbia case, empirical F value is 10.141 and p = 6.13308E−06, indicating that the differences between the indexes are statistically significant.
ANOVA | Indexes | t (Dt = 9) | Sig. (2-tailed) | Mean difference | ||||||
---|---|---|---|---|---|---|---|---|---|---|
SS | df | MS | F | p-value | F crit | RXA | 4.909 | .001 | 5.28300 | |
Between groups | 211.2157 | 4 | 52.80392 | 10.14101 | 6.13308E−06 | 2.578739 | RMA | 3.527 | .006 | 0.04200 |
Within groups | 234.3136 | 45 | 5.206969 | RTA | 4.889 | .001 | 5.24400 | |||
Total | 445.5293 | 49 | RC | 12.326 | .000 | 5.09500 | ||||
RCA | 7.816 | .000 | 2.74800 |
ANOVA and one sample test for RXA, RMA, RTA, RC and RCA indexes for Serbia in the period from 2006 to 2015.
According to research conducted [19, 21, 22], we performed a correlation analysis of the obtained indexes, to examine the extent to which the indices related to the identification of comparative advantages (Table 7). By using Pearson (rp) and Spearman (rs) test of correlation, we have proved the existence of correlation between 10 paired samples, that is, found how much the Balassa index values covariate. For Serbia, we have two pairs with strong positive correlation with p-value below .01 so we can conclude that a correlation exists and the variables covariate. Test of connection of the Balassa index using Spearman formula shows that there is a correlation in three pairs with p-value below .1 and one pair with p-value below .05, so we can conclude that a correlation exists and that the variables covariate. It is interesting to conclude that there is a correlation right between relative export advantage and relative trade advantage, as well as between the found competitiveness and the found comparative advantage, which points to the conclusion that each growth in honey exports has positive effects on the growth of comparative advantage of exports.
\n\nPearson correlation | Spearman’s correlation | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Pearson correlation | RXA | RMA | RTA | RC | RCA | RXA | RMA | RTA | RC | RCA | |
RXA | Pearson correlation | 1 | .268 | 1.000** | .537 | .589 | 1.000 | .228 | 1.000** | .455 | .794** |
Sig. (2-tailed) | .455 | .000 | .110 | .073 | .527 | .187 | .006 | ||||
N | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | |
RMA | Pearson correlation | .268 | 1 | .258 | −.612 | −.347 | .228 | 1.000 | .228 | −.579 | −.160 |
Sig. (2-tailed) | .455 | .472 | .060 | .326 | .527 | .527 | .080 | .659 | |||
N | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | |
RTA | Pearson correlation | 1.000** | .258 | 1 | .545 | .594 | 1.000** | .228 | 1.000 | .455 | .794** |
Sig. (2-tailed) | .000 | .472 | .104 | .070 | .527 | .187 | .006 | ||||
N | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | |
RC | Pearson correlation | .537 | −.612 | .545 | 1 | .883** | .455 | −.579 | .455 | 1.000 | .758* |
Sig. (2-tailed) | .110 | .060 | .104 | .001 | .187 | .080 | .187 | .011 | |||
N | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | |
RCA | Pearson correlation | .589 | −.347 | .594 | .883** | 1 | .794** | −.160 | .794** | .758* | 1.000 |
Sig. (2-tailed) | .073 | .326 | .070 | .001 | .006 | .659 | .006 | .011 | |||
N | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 |
Pearson (rp) and Spearman (rs) test of correlation indexes for Serbia.
*Correlation is significant at the 0.05 level (2-tailed).
**Correlation is significant at the 0.01 level (2-tailed).
In a study of comparative advantage in exports of honey from Romania, we applied five indexes: relative advantages of export, relative import advantage, relative trade advantage, the revealed competitiveness and the Balassa index, as for Serbia. Table 8 shows the indexes for all analysed years. The research found a positive comparative advantage of all five indices. Empirical research results of comparative advantage in exports of honey from Romania in the period 2006–2015 are shown in Table 8.
N | Minimum | Maximum | Mean | Std. deviation | Variance | |
---|---|---|---|---|---|---|
RXA | 10 | 5.26 | 10.12 | 7.4340 | 1.53225 | 2.348 |
RMA | 10 | .05 | .87 | .4690 | .27111 | .073 |
RTA | 10 | 4.63 | 9.84 | 6.9660 | 1.65515 | 2.740 |
RC | 10 | 1.97 | 5.30 | 3.0000 | .98958 | .979 |
RCA | 10 | 1.50 | 3.08 | 2.0330 | .47070 | .222 |
Descriptive statistics for RXA, RMA, RTA, RC and RCA indexes of exports of honey from Romania in the period 2006–2015.
Source: ITC and calculation of the author.
Results of the research of comparative advantage of honey exports from Romania show that all the five indexes reached positive values (Table 9). The highest level has been achieved with relative export advantage, which caused a high positive trade advantage. Relative import advantage has very low, however positive value, which points to the presence of small quantities in imports. We have concluded that Serbia and Romania have similar export structure regarding the honey exports, which points to high values in comparative advantage. Analysis of variance in the case of Romania shows the following: empirical F value is 74.51792 and p = 2.91E−19, indicating that differences between groups are statistically significant, systematic.
ANOVA | Indexes | t (Dt = 9) | Sig. (2-tailed) | Mean difference | ||||||
---|---|---|---|---|---|---|---|---|---|---|
SS | Df | MS | F | p-value | F crit | RXA | 15.342 | .000 | 7.43400 | |
Between groups | 379.2464 | 4 | 94.8116 | 74.51792 | 2.91E−19 | 2.578739 | RMA | 5.471 | .000 | .46900 |
Within groups | 57.25498 | 45 | 1.272333 | RTA | 13.309 | .000 | 6.96600 | |||
Total | 436.5014 | 49 | RC | 9.587 | .000 | 3.00000 | ||||
RCA | 13.658 | .000 | 2.03300 |
ANOVA and one sample test for RXA, RMA, RTA, RC and RCA indexes for Romanian the period from 2006 to 2015.
By using Pearson (rp) and Spearman (rs) test of correlation, we have proved the existence of correlation between 10 paired samples (Table 10). For Romania, we have six pairs with strong correlation with p-value below .01 so we can conclude that correlation exists and the variables covariate. One pair showed negative correlation, which points to the fact that the increase of imports, that is, relative import advantage has negative effects upon revealed competitiveness. Test of connection of the Balassa index using Spearman formula shows that there is a correlation in four pairs with p-value below .1, so we can conclude that correlation exists and that the variables covariate. There is also a negative correlation with relative import advantage and revealed competitiveness.
Pearson correlation | Spearman’s correlation | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
RXA | RMA | RTA | RC | RCA | RXA | RMA | RTA | RC | RCA | ||
RXA | Correlation | 1 | −.386 | .988** | .574 | .803** | 1.000 | −.261 | .964** | .467 | .891** |
Sig. (2-tailed) | .271 | .000 | .083 | .005 | .467 | .000 | .174 | .001 | |||
N | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | |
RMA | Correlation | −.386 | 1 | −.521 | −.901** | −.557 | −.261 | 1.000 | −.430 | −.952** | −.430 |
Sig. (2-tailed) | .271 | .122 | .000 | .094 | .467 | .214 | .000 | .214 | |||
N | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | |
RTA | Correlation | .988** | −.521 | 1 | .679* | .834** | .964** | −.430 | 1.000 | .624 | .903** |
Sig. (2-tailed) | .000 | .122 | .031 | .003 | .000 | .214 | .054 | .000 | |||
N | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | |
RC | Correlation | .574 | −.901** | .679* | 1 | .792** | .467 | −.952** | .624 | 1.000 | .588 |
Sig. (2-tailed) | .083 | .000 | .031 | .006 | .174 | .000 | .054 | .074 | |||
N | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | |
RCA | Correlation | .803** | −.557 | .834** | .792** | 1 | .891** | −.430 | .903** | .588 | 1.000 |
Sig. (2-tailed) | .005 | .094 | .003 | .006 | .001 | .214 | .000 | .074 | |||
N | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 |
Pearson (rp) and Spearman (rs) test of correlation indexes for Romania.
*Correlation is significant at the 0.05 level (2-tailed).
**Correlation is significant at the 0.01 level (2-tailed).
In a study of comparative advantage in exports of honey from Italy, we applied five indexes: relative advantages of export, relative import advantage, relative trade advantage, the revealed competitiveness and the Balassa index, as for Serbia and Romania. Table 11 shows the indexes for all analysed years. In our research, we have found positive average value for relative advantages of export and relative import advantage. Empirical research results of comparative advantage in exports of honey from Italy in the period 2006–2015 are shown in Table 11.
N | Minimum | Maximum | Mean | Std. deviation | Variance | |
---|---|---|---|---|---|---|
RXA | 10 | .43 | 1.04 | .6360 | .19540 | .038 |
RMA | 10 | .75 | 1.57 | 1.1880 | .25354 | .064 |
RTA | 10 | −.86 | −.26 | −.5520 | .20460 | .042 |
RC | 10 | −1.06 | −.34 | −.6450 | .24451 | .060 |
RCA | 10 | −1.06 | −.26 | −.6260 | .24139 | .058 |
Descriptive statistics for RXA, RMA, RTA, RC and RCA indexes of exports of honey from Italy in the period 2006–2015.
Source: ITC and calculation of the author.
Results of the research of comparative advantage of honey exports from Italy show that positive values have been achieved in two indexes, that is, relative export and import advantage have positive average values (Table 12). As the relative import value grows, the level of relative trade openness, as well as the revealed competitiveness and the Balassa index become negative. Analysis of variance in the case of Italy shows the following: empirical F value is 139.512 and p = 9.51E−25, indicating that differences between groups are statistically significant, systematic.
ANOVA | Indexes | t (Dt = 9) | Sig. (2-tailed) | Mean difference | ||||||
---|---|---|---|---|---|---|---|---|---|---|
SS | Df | MS | F | p-value | F crit | RXA | 10.293 | .000 | .63600 | |
Between groups | 29.28445 | 4 | 7.321112 | 139.5118 | 9.51E−25 | 2.578739 | RMA | 14.817 | .000 | 1.18800 |
Within groups | 2.36145 | 45 | 0.052477 | RTA | −8.532 | .000 | −.55200 | |||
Total | 31.6459 | 49 | RC | −8.342 | .000 | −.64500 | ||||
RCA | −8.201 | .000 | −.62600 |
ANOVA and one sample test for RXA, RMA, RTA, RC and RCA indexes for Italy in the period from 2006 to 2015.
By using Pearson (rp) and Spearman (rs) test of correlation (Table 13), we have shown that for Italy we have six pairs with strong correlation, at the level of p < .01 and .05 so we can conclude that the correlation exists and variables covariate. One pair showed negative correlation, which points to the fact that the increase in imports, that is, relative import advantage has negative effects upon revealed competitiveness. Test of connection of the Balassa index using Spearman formula shows that there is a correlation in five pairs with p < .01 and .05, so we can conclude that correlation exists and that the variables covariate. There is also a negative correlation with relative import advantage and revealed competitiveness (RC).
Pearson correlation | Spearman’s correlation | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
RXA | RMA | RTA | RC | RCA | RXA | RMA | RTA | RC | RCA | ||
RXA | Correlation | 1 | .602 | .197 | .669* | .741* | 1.000 | .503 | .311 | .775** | .801** |
Sig. (2-tailed) | .066 | .585 | .034 | .014 | .138 | .382 | .008 | .005 | |||
N | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | |
RMA | Correlation | .602 | 1 | −.664* | −.160 | −.062 | .503 | 1.000 | −.590 | −.037 | .024 |
Sig. (2-tailed) | .066 | .036 | .658 | .866 | .138 | .072 | .920 | .947 | |||
N | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | |
RTA | Correlation | .197* | −.664* | 1 | .826** | .772** | .311* | −.590 | 1.000 | .761* | .750* |
Sig. (2-tailed) | .585 | .036 | .003 | .009 | .382 | .072 | .011 | .012 | |||
N | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | |
RC | Correlation | .669* | −.160* | .826** | 1 | .986** | .775** | −.037* | .761* | 1.000 | .976** |
Sig. (2-tailed) | .034 | .658 | .003 | .000 | .008 | .920 | .011 | .000 | |||
N | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | |
RCA | Correlation | .741* | −.062 | .772** | .986** | 1 | .801** | .024 | .750* | .976** | 1.000 |
Sig. (2-tailed) | .014 | .866 | .009 | .000 | .005 | .947 | .012 | .000 | |||
N | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 |
Pearson (rp) and Spearman (rs) test of correlation indexes for Italy.
*Correlation is significant at the 0.05 level (2-tailed).
**Correlation is significant at the 0.01 level (2-tailed).
In our further research, we wanted to determine the value of importance of the difference between the comparative advantage indexes (RCA) for all three countries (Table 14). The variance analysis shows the following: the empirical F value is 62.554 and p = 7.32E−11, which points to the fact that differences between the groups are statistically significant.
Source of variation | SS | df | MS | F | p-value | F crit | t (Dt = 9) | Sig. (2-tailed) | Mean difference | |
---|---|---|---|---|---|---|---|---|---|---|
Between groups | 63.21794 | 2 | 31.60897 | 62.55435 | 7.32E−11 | 3.354131 | RCA SRB | 7.816 | .000 | 2.74800 |
Within groups | 13.64321 | 27 | 0.505304 | RCA RO | 13.658 | .000 | 2.03300 | |||
Total | 76.86115 | 29 | RCA IT | −8.201 | .000 | −.62600 |
ANOVA test RCA index for Serbia, Romania and Italy.
Results of further analysis show that there is statistically significant difference between the levels of comparative advantage of exports between Serbia and Italy and Romania and Italy (Table 15). If we consider that Serbia and Romania are producing surplus in international honey trade, while for Italy, imports are more important, then the empirical results prove the absence of the comparative advantage in honey exports in Italy.
Paired samples test | |||||||||
---|---|---|---|---|---|---|---|---|---|
Paired differences | t | df | Sig. (2-tailed) | ||||||
Mean | Std. deviation | Std. error mean | 95% confidence interval of the difference | ||||||
Lower | Upper | ||||||||
Pair 1 | RCA_SRB–RCA_RO | .71500 | 1.40725 | .44501 | −.29168 | 1.72168 | 1.607 | 9 | .143 |
Pair 2 | RCA_SRB–RCA_IT | 3.37400 | 1.09891 | .34751 | 2.58789 | 4.16011 | 9.709 | 9 | .000 |
Pair 3 | RCA_RO–RCA_IT | 2.65900 | .57922 | .18317 | 2.24465 | 3.07335 | 14.517 | 9 | .000 |
Paired samples test RCA index for Serbia, Romania and Italy.
Research results point to a mild increase in world production. The production increase has positive consequences in the expansion of honey exports in some European Union countries. Honey trade and competitive patterns presented in previous tables (Sections 3 and 4) have shown clearly different trends and exchange structures in the three selected countries examined (Serbia, Romania and Italy). While Serbia and Romania are net exporter, with a positive trade balance, Italy is a net importer, with negative balance given the deficit of export compared to import. Romania and Serbia differ for the trade volume (import + export) that is about seven times bigger in Romania (on average 2006–2015). Romania has a growing market and a lot of opportunities for export. Nevertheless, even if Serbia has a smaller market, the low levels of import may suggest that domestic beekeeping industry is able to cover both internal and foreign demand (even if some other causes, like trade barriers, may have a role). Such surprising low impact of import is confirmed by previous analysis on honey consumers in Serbian regions. Ref. [20], in fact, suggest: “While researching consumer attitudes, we have come to the conclusion that the majority of consumers, as many as 83%, are willing to try Fruska Gora’s lime honey that is of above average quality and is certified, regardless of the fact that lime honey does not belong to the type of honey which consumers buy. The reason for this lies in the fact that consumers prefer a high quality of honey because it is linked to better taste and better healing properties of honey, which is in line with the motivations of consumers. For such a quality and certified honey, consumers are willing to pay even a 30% higher price than the average market price of lime honey.”
\nApparently the opposite applies to Italy whose internal demand for honey is covered to a relevant extent by imports. This may be seen as a source of potential unexplored demand to be covered by Italian beekeepers. Both, Romania and Serbia have a high coverage of imports by exports and a positive comparative advantage of export. Such evidences are also confirmed by previous studies on competitiveness of Countries in the Danube regions. For example the study [23] concluded that “In the following commodity groups in Serbia an increase of positive comparative advantage is present: milk and products, except butter and cheese; butter and other fats from milk; dairy spreads, cheese and curd, products of cereals, flour, starch; vegetables, roots and tubers, processed; fruit prepared and products; sugar, molasses and honey; chocolate and other food preparations with cocoa”. In another analysis [21] have been using D’Agostino and Pearson omnibus normality test showing that the Balassa index value distribution in Romania does not deviate significantly from normality (K2 = 2.46 and p = .29).
\nRomania is a net exporter of honey, well known at international level. The introduction of quality standards and the certification of honey will increase the prices of commercialised honey, mostly for the external market. Beekeepers should maintain the quality of honey by preserving the environment and the traditional practices. The production of organic honey is another sector that brings a comparative advantage for Romania and creates the possibility to develop a niche market. Anyway, Romania could represent a model of good practices for Serbia to improve the competitiveness of the beekeeping sector through innovation and associative forms [24–26].
\nResearch has also shown that the majority of European Union countries imported honey. Italy is a net importer and has a large trade deficit in terms of value and volume, even if such gap is slightly declining. Italian beekeeping industry should take actions to recover market shares of domestic demand, developing more effective promotional activities towards consumers. The conclusion of the study reveals the fact that innovation through the whole value chain is one of the key factors for increasing competitiveness of honey production and trade. Interestingly, the results of the present analysis are also confirmed and mirrored by previous studies that point out the importance of honey quality improvement as an essential way to pursue both sector innovation and product promotion.
The oral-gastro-intestinal-sex-skin can be classified as unique large and heterogeneous apparatus populated by a huge variety of microorganisms, bacteria, virus, fungi, and other single-celled creatures, that compose the totality of human microbiota that contributes together with bone/skeleton system, to maintain the body energy homeostasis. The human body hosts something like 10–100 trillion microbial cells that coexist in a strict fruitful symbiotic relationship that persists as long as the body is kept in a balanced healthy state [1, 2]. The gut plays an important role in regulating metabolic immune activities. The gut’s essential task is the absorption of nutrients and the synthesis of important micromolecules obtained from food that cannot be assimilated by the stomach and small intestine [1, 2, 3]. Xyloglucans and fructo-oligosaccharides from vegetables and fruits, protein, and lipids; the assimilation of essential vitamins like vitamins B-12, D, and K; and the synthesis of hormones like serotonin from tryptophan amino acid take place right in the gut, thanks to the constant activity of its entire microbiota. The microbiota are able to produce 50–100 mmol·L—1 per day of extremely important short-chain fatty acids (SCFAs), such as acetic, propionic, and butyric acids—and serve as an energy source to the host intestinal epithelium and skeleton [1, 2, 3, 4].
The importance of SCFAs has been well described by several studies during the last decade; the activity of acetic acid, for instance, has been found to be essential against infections, in blood pressure regulation and against sclerotic plaque deposition in arterial walls. The presence of butyric acid is an essential anti-IBS agent due to its immune-modulator properties and anti-inflammatory action, while propionic acid has been found to be important in preventing obesity and diabetes 2 [1, 2, 3, 4].
Although bacteria, viruses, and fungi might be very harmful and dangerous, they are indispensable for life as well. This symbiotic coexistence throughout the millennia made a deep crucial biological impact on human species, and it has become essential not only for survival but for evolution as well. Accumulating evidences have clearly demonstrated how part of these specific microorganisms can resume specific immunomodulatory roles and the way they affect either composition function or migration of various immune cell subpopulations from one site to a different location. For instance, oral macrophages may migrate under the influence of specific signal induction of local microbiota from oral either to the lungs or even the brain passing through the blood brain barrier (BBB) [5, 6, 7, 8, 9, 10].
The outcomes from experiments performed on germ-free (GF) mice confirmed the great role of gut microbiota in the upsurge of immune system deficiencies. GF animals were shown to have compromised Paneth cells and low levels of natural killer (NK) cells, dendritic cells (DCs), and α/β + and γ/δ + T cell populations that play an important role in defense and pathogenesis during inflammation and infection, especially against certain types of malignancies. In addition, GF animals were highly susceptible to frequent infections due to a decline in angiogenin-4 (Ang4), a powerful antimicrobial part of the class of microbicide proteins in Paneth cells [5, 6, 7, 8, 9, 10].
The alteration of the gut microbiota may contribute to open up the invasion of exogenous pathogens that may destabilize the whole intestinal mucosa. The pathogen systematic overgrowth will trigger a cascade of strong inflammatory responses making intestinal mucosa highly susceptible and motile. The chronic inflammation will weaken the endothelial tide junction to the point that the walls become highly relaxed and permeable causing the phenomenon known as “leaky gut” that allows the free, uncontrolled passage of microorganism into the system via the bloodstream and tissues where they start allocating. In fact, the presence of these typical gut residents could be found in eroded, inflamed, and degenerated joints and organs such as the lungs, heart, brain, and liver [11, 12, 13].
The high and uncontrolled levels of pathogenic microorganism colonizing the gut contribute to a condition known as dysbiosis [14]. Since few years the dysbiosis has been associated with a variety of degenerative patterns that tend to subvert the metabolic/neuro/hormonal/immune axis contributing to a variety of disorders that round to different body systems ranging from skeleton, cardiovascular, to neuro system. There are several mechanisms proposed that are able to trigger this state of systemic disorders; one of the possibilities is linked to bacterial metabolites and immune-modulating mediators that contribute to the high permeability of intestinal mucosa allowing local pathogens to get through the mucosal barriers triggering a huge variety of immune responses. A second and though related mechanism is the sabotage of SCFAs’ production; the consequences of this mechanism are the abrupt breakdown of energy balance mechanism, a reduction of cell-bacteria signaling pathway, and the worsening of epithelial cell layer integrity due to the decreased production of tight junction proteins which allows the translocation of LPS into the submucosa as well. The significant presence of pro-inflammatory cytokines and interleukins such as TNFα, IFN-γ, IL-1β, IL-2, IL-4, IL-5, and IL-6 is the peculiar trait of a dysbiotic gut (Figure 1) [14, 15, 16]. A third way of dysbiosis transmission is through the vagus nerve (VN), the main component of parasympathetic nervous system (PSN) which also constitutes an effective bridge of the gut/CNS axis. This hypothesis, today supported by a concrete line of evidences, proposes the existence of a reciprocal interference way between the CNS and gut through the VN. In this view the VN is able to perceive microbiota movement, grade of activity, and therefore degradation; on the other hand, pathogens once out from the gut mucosa barrier are able to communicate and move to the CNS through the VN pathway [14, 15, 16, 17, 18].
The gut microbiota is a very dynamic ecosystem. The entire gut microbiota is composed of different sub-environments with unique features like niches with specific microbes and tissue interactions. The large intestine represents the more populated area and performs the highest variety of biotransformation under the guide of specific gene expression in charge of enzymes necessary for highly specific biotransformation necessary of the SCFAs. The local flora is crucial for the local microbiome homeostasis, and the whole chain of bio-reaction takes place in spaces with a specific mean pH of 6.5–7. The changes in local balance homeostasis and in pH negatively impact on the mucosa shield that repair the outside and inside permeability gradient. Once the stability and the equilibrium between all the components are broken, the gut walls become fragile under the constant attack of local immune cells that start to deteriorate the integrity of both endothelial wall and mucosa shield that induce on medium long term and accumulation of pro-inflammatory endotoxins, bacteria free passage into the system, and low antimicrobial peptide production with a consequent high gastrointestinal motility.
These essential structural alterations are at the base of neurodegenerative pathologies. Though it is a unique pathological aspect, we may see the presence of a common configuration indeed, which is a shared neurological chronic inflammatory pattern. In all these cases, the chronic neuro-inflammatory condition is characterized by an abnormal hyperactive behavior of neural immune cells, the microglia, known as macrophages of the brain [18, 19]. The chronic inflammatory state that from the gut opens up the pathway of pathogenic microbiota invasion all the way through oral and brain compartment, which is the hallmark of neurodegenerative disorders’ dynamic pathogenesis. Patients with Parkinson’s disease (PD), Alzheimer’s (AD), multiple sclerosis (MS), or amyotrophic lateral sclerosis all present a variety of disturbances in intestinal microbial compared to healthy individuals. Neurodegenerative-affected patients’ intestinal and fecal analysis showed a clear clinical picture of microbiota dysbiosis. The test outcomes showed high level of coliform and gram-negative bacteria from Ralstonia genus concomitantly with low critical level of anti-inflammatory strains related to Blautia, Coprococcus, and Roseburia genera. Another indicator was also noted; it was the low presence of Prevotella generally seen as beneficial bacteria, involved in the metabolism of plant polysaccharides and vitamins strictly associated with the production of neuroactive SCFAs, such as GABA [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]. The Prevotella spp. is associated with mucin-type O-glycan production which is extremely important in the integrity of gut epithelial barrier; the absence of this mucin type (mucin-2 specifically) tends to compromise the correct homeostatic balance of the local microbiota, increasing intestinal permeability, a clinical feature associated with both microbial dysbiosis and neurodegenerative diseases [32, 33, 34].
Disruption of the BBB is a hallmark in individuals with neurodegenerative diseases that contributes to a steady and progressive death of dopaminergic neurons in the CNS. The BBB is a part of a systemic condition that eventually allows the invasion of pathogens and immune agents from a dysbiotic gut into the CNS. However, damages are also due to a series of changes that weaken the integrity of microvasculature and blood vessels; these modifications are mainly due to nutritional impairment as a consequence of gut microbiota disturbances that cause low-level intake of important nutrients. Deficiencies in vitamins like C, K, D, and folates responsible for low hydroxylation for the formation of chondro-sulfate necessary for healthy microvessel endothelial walls, the augmentation of free radicals, and depletion in oxygen contents and nitrogen, matrix metalloproteinases (MMPs), cyclooxygenases (COXs), and hypoxia-inducible factor-1α (HIF-1α) are all linked with BBB disruption as neuro-inflammatory responses tend to increase and evolve [35].
Thus the scenario existing in the great majority of neurodegenerative pathologies presents a combination of higher permeability of the intestinal barriers and the BBB, inducing a greater access between gut microbiota and the CNS compartment. Experiments conducted with the use of high dose of minocycline antibiotic are well known to have an impact on specific gut and oral invasive bacteria; the post-administration results showed significant protection on LPS-induced PD in mice data confirmed by a significant amelioration of neuro-inflammatory markers such as TNF-α expression, IL-1α expression, and microglia activation and a substantial amelioration of astrocyte loss with an increased number of surviving dopaminergic neurons compared to control LPS only-injected mice [36, 37]. It follows that a correct use of antibiotics generally known to alter gut microbial diversity may disclose a positive immune protective side effect on inflammatory mechanism existent in PD patients [36, 37, 38]. Several other outcomes have shown the beneficial effects of oral antibiotic, minocycline, and tetracycline, in CNS degenerative condition like the experimental autoimmune encephalopathy (EAE) disease and MS. It was found a significant increase of IL-10 expression concomitant with a favorable increase of a subset of invariant NK T cells and in patients with MS, and there was a substantial reduction of CNS deteriorations [38, 39, 40, 41, 42].
Aging brings generally substantial physiological alterations—hormonal, humoral, and physical—that involves the entire homeostatic organization of the human body. Of course the GI tract and its microbiota as well undergo through profound changes that under the variations of dietary influences bring to a general decline of cognitive and immune activities. With aging, the gut microbiota lost bacterial balanced diversity with an increase of “pathogenic Proteobacteria” vs. a continuing, steady, and progressive lower level of “friend bacteria” such as Firmicutes, Faecalibacterium prausnitzii, and Actinobacteria (mainly bifidobacteria) [42, 43, 44, 45].
Another important feature of gut microbiota is the ability to modulate genes that can be seen either on regulation or variation; this is one of the main factors that may explain the influences that gut microbiota eventually exert on bone development and on bone-related diseases such as osteoporosis, osteopenia, or the different types of arthritis. The delicate homeostatic balance that regulates bone formation and resorption is partly played by the activity of intestinal microorganisms. This activity is basically performed through the interaction with endocrine/nervous system axis; thereby the hormonal activity such as serotonin, cortisol, and sex hormones and several growth factors affect bone mass in mice and humans. In addition, bone marrow stem cells, circulatory stem cells, and stem cells from bone marrow niche are highly sensitive to gut microenvironment condition which eventually affects the differentiation process toward either osteoblasts or osteoclasts. In this case it has been proven that the metabolic pathway compartment which involves the ribosome activity, glycolysis, oxidative phosphorylation, carbon metabolism, and mitochondria ATP are fully responsible of regulating MSCs’ functionality, growth, proliferation, and differentiation [45, 46].
This important connection has also been confirmed by Xiao and colleagues; they were able to highlight through the single-cell RNA-sequencing analysis the existing connection between the gut microbiota, BM-MSCs, and bone metabolic functionality. The presence of several factors such as the HIF-1 together with the expression of infection/inflammatory signaling pathways could be the scattering patterns that influence MSC mobility and immunomodulation. These outcomes showed how HIF-1 signaling is involved in BM-MSC immunomodulation. In fact, the HIF-1 is notoriously known as a triggering factor of inflammatory transcription factor NFκB and an active regulator of specific cytokine and chemokine recruitment in inflammation and infection situations. The chronic presence of an inflammatory response under the triggering activity of pro-inflammatory cytokines such as the TNF superfamily IL-1β, IL-2, IL-4, IL-6, and IL-17 can deeply disturb the osteoclast and osteoblast balance, typically resulting in a net hyper-osteoclast activity and thus bone loss. While there is an evident mechanical effect on the bone where these cytokines stimulate osteoclast differentiation with a consequent upregulation of RANKL expression in progenitor osteoblasts together with a higher RANKL expression, a concomitant nonmechanical effect under the downregulation of specific deficit due to a metabolic inability of vitamin K and vitamin D synthesis in the intestinal lumen should be mentioned (Figure 2) [47, 48, 49, 50, 51, 52, 53, 54].
The gut dysbiosis is one of the main contributors in osteo-degenerative conditions. The dysbiotic microenvironment increase the viability of systemic pro-inflammatory cytokines and interleukins generating three main anti-regenerative patterns, the increase of pH acidic level, decrease of the differentiating pathway from MSCs and SCs toward osteoblasts, and hyper-expression of osteoclast activity. The dysbiosis generates a defective absorption mechanism of important nutrients for bone homeostasis like vitamins, among them K and D, and hormones such as serotonin, testosterone, and estrogen. The prerogative of this condition is a cascade of events that will involve systemically and progressively the whole vital activity of cells, tissues, and systems of the organism.
The potential impact of the therapeutic effect of probiotic on a dysbiotic situation could not be seen without taking in consideration a change of lifestyle. Diet habits, stress, poor healthy conditions, and lack of exercise can significantly impact the gut microbiota stability [55, 56, 57]. There are many evidences that nutritional habits based on “Western diet” composed of huge additive and animal fats, processed glucose and excessive quantities of hypercaloric nutritional facts, low contents of fresh food, and low level of vitamins and minerals, essential for our body, all negatively affect the correct balance of gut microbiota, which eventually lead to the insurgence of metabolic dysfunctions. It is also well known that these bad behaviors are associated with an increased risk of developing several chronic diseases that may attack oral microbiota and vaginal microbiota that recent study findings have indicated as an independent risk factor for severe neurodegenerative conditions [9, 58, 59, 60].
By definition, probiotic refers to large and diverse types of microbes both commensal that normally reside in the gut and exogenous that may migrate through the intestine following food or diet and supplement consumption. Probiotics might be composed of different microbial strains, the most common include species of Lactobacillus, Bifidobacterium, Streptococcus, and yeast Saccharomyces species [61].
As previously mentioned, currently there is a great interest on the use of specific probiotics as therapeutic tool to be associated as clinical approach toward immune system pathologies that may include autoimmune conditions that may attack nerves, bone, and bowel. Given the prevalence of probiotic use, the effects of probiotics on bone health is of significant interest.
Significant positive clinical outcomes have been obtained in numerous studies conducted on CNS inflammatory condition that have therapeutically used different types of probiotic strains. The results showed a reduction of CNS inflammatory level and progression; these outcomes were eventually explained by the capacity of certain strains (Lactobacillus species including bacteria like the Pediococcus acidilactici, Bifidobacterium bifidum, Bifidobacterium animalis, Streptococcus thermophiles, and Bifidobacterium infantis 35,624) to modulate the expression of T-regs, B-regs, and IL-10 production such as [62, 63, 64].
In addition, an experiment with genetically engineered microbial strains such as Lactococcus lactis capable of expressing heat shock protein 65 obtained from another strain like the Mycobacterium leprae was seen highly efficient in reducing EAE symptoms and disease progression [63, 64]. The beneficial outcomes in this study were associated with a decrease in IL-17 pro-inflammatory interleukin with a parallel growth of IL-10 evaluated in the mesenteric lymph node and spleen cell cultures. Furthermore, mice showed a significant higher level of endogenous CD4 + Foxp3+ regulatory T-regs and CD4 + LAP+ (latency-associated peptide). These results might be also sustained by a higher production of SCFAs that, as stated by Opazo and colleagues, were seen to induce either a decrease in RORγt, a biomarker of IL-17, or IL-23 with a higher production of IL-10 and IL-12 with a similar beneficial effect on both EAE and IBS [61, 62, 63, 64, 65, 66].
Therefore beneficial homeostatic-metabolic effect of specific probiotic strains can be seen on different systems such as the cardiovascular, immune, and CNS. For instance, few strains conserve a natural ability of inhibiting the insurgence of hypercholesteremia in both mice and human. In fact the use of Lactobacillaceae strains such as L. acidophilus, Bifidobacterium bifidum, and L. plantarum Lp9 strain showed a significant role in lowering the cholesterol level under in vivo conditions thanks to their ability of secreting functional bile salt hydrolase (BSH), an enzyme crucial in the protection against the insurgence of bad cholesterol in the host. Genomic analysis has indicated that Lactobacillaceae especially L. plantarum contain the highest presence of BSH genes. Intriguingly, milk fermented by L. plantarum NTU 102 revealed to have a high significant efficacy on total cholesterol and LDL cholesterol levels though in presence of individuals undergone a cholesterol-rich diet [67, 68, 69, 70].
Major depressive disorders have been seen even today as a consequence of decreased serotonin level; therefore, the therapeutic strategy has mainly concentrated on producing medication, which focused on serotonin only. The major treatments are based on a class of drugs known as selective serotonin reuptake inhibitors (SSRIs). These SSRIs stimulate the serotonin uptake between neurons, and, though it has been seen some improvements, the medium long-term use has produced serious side effects on gut homeostatic balance with severe metabolic disturbances. Nowadays, as above mentioned, following the fact that current researches have established associations between gut microbes, digestive function, and mental well-being especially under the fact that serotonin is synthesized in the gut by the combined activity of different microbiota strain such as Lactobacillus. The connection was firstly seen in IBS patient who also manifests clear clinical signs of depressive disorders; the analysis of gut microbiota from these patients showed a very low level of Lactobacillus strains versus healthy subjects that might be explained by the increased expression of serotonin transporter (SERT) [71, 72, 73, 74].
Overall the data on this specific topic all have evidenced the positive effects of probiotics in CNS health. These effects are explained by the ability of probiotics to directly interact with fundamental metabolic agents either within the gut or outside that eventually explain the Gut-CNS axis. The use of probiotics and in specific the Lactobacillus strains showed that mice fed with these probiotics revealed a better capacity of reabsorbing tryptophan amino acid the precursor or serotonin, the re-established normal level of those hormones strictly related to stress deviances and depression such as the adrenocorticotropic hormone (ACTH), corticosterone, adrenaline, noradrenaline and the re-increase expression of brain-derived neurotrophic factor (BDNF) a marker that indicate a neuronal health and memory functionality [74, 75, 76, 77, 78, 79].
To conclude, the higher permeability of gut or “leaky gut” intensifies the fee passage of endotoxins such as the LPS and other forms of molecules and pathogens to leak into the bloodstream and thus in the entire system. The upsurge of these endotoxins, pathogens, and waste molecules eventually trigger the activation of a cascade of immune responses through switching on the Toll-like receptor 4 (TLR4) that mediates the recruitment of T and B lymphocytes together with a huge number of pro-inflammatory cytokines, interleukins, and IgA (Figure 3) [80, 81]. The current position therefore considers the use of probiotics as a therapeutic tool that may exert beneficial effects on the CNS by improving the stability homeostasis and integrity of gut microbiota, decreasing systemic inflammation.
There is a strict connection between CNS and gut system. The connection takes place through the afferent and efferent pathways of vagus nerve that physically connects both CNS and gut. Both CNS and gut may undergo leaky phenomena; in both cases, the barriers of either CNS or gut become extremely permeable under the chronic attack of both pathogens and immune agents overexpressed on the site. This event may eventually explain degenerative condition of both systems including IBS, ulcerative colitis, depression, PD, AD, and MS.
The skin represents another system where an immense variegation of microbiota environment can be found. Skin diseases caused by disturbances at the level of local microbiota that also showed to have strict connection with gut dysbiosis are quite exhaustive in explaining these malevolence patterns. Psoriatic lesions show a very specific histopathological conformation which present highly infiltrated immune cells like the CD3+ T cells and dendritic cells (DCs). Psoriasis showed to have a genetic family trait prevalent in twins; researchers have spotted 36 genetic loci associated with PS susceptibility 1 (PSORS1) locus on chromosome 6p21.3 [82, 83]. Data confirmed that most of them are directly involved in the overexpression of those genes that regulate part of pro-inflammatory innate immune responses such as the NFkB activation and interleukin (IL)-23 signaling pathway. Intriguingly a 2018 study performed on mice proved the use of two specific probiotic Lactobacillus strains, the L. salivarius L305 and L. rhamnosus L307, in alleviating the clinical symptoms of psoriasis through inhibiting the aggressive effect of pro-inflammatory cytokines and interleukins like TNF-α, IL-1β, IL-6, IL-17, and IL-22 and promoting the anti-inflammatory/modulatory activity of IL-4 and IL-10 [84].
In oral dysbiosis, we are facing a similar inflammatory arrangement; oral diseases manifest with high-grade inflammatory patterns that spread from the gums to the adjacent structures gradually destroying the supporting tissues of the teeth, both ligaments and alveolar bones, causing early loss. Similarly to psoriasis in periodontitis, we may encounter multifactorial condition due to a combination of genetic variants triggered by the initial subgingival dysbiosis and then become highly susceptible to wider disease progression [85].
The gram-negative bacteria such as Porphyromonas gingivalis, Tannerella forsythia, and Aggregatibacter actinomycetemcomitans are be able to migrate into the system either down to the heart, lungs, and sex apparatus or are capable to enter into the brain via the bloodstream or via infected periodontal sites. Histopathological analysis has confirmed these bacteria almost everywhere in atheromatous plaques, the vagina, amniotic fluid, rheumatoid arthritis bioptic samples, and brain plaques typical of neurodegenerative diseases AD, PD, and MS [86, 87, 88, 89, 90].
As can be seen from published studies, different strains of probiotics have been used for the treatment of periodontitis. Lactobacillus strains are the commonest used in the majority of high-grade inflammatory disease. The use of L. salivarius in combination with L. rhamnosus and B. subtilis together with L. reuteri and L. brevis probiotics has shown the most promising results. High-positive results were also obtained by Laleman and colleagues in using Streptococcus oralis KJ3, Streptococcus uberis KJ2, and Streptococcus ratti JH145 [91, 92].
Therefore an associated altered gut microbiota may lead to chronic gut dysbiosis and propagation of systemic injuries that involve cells, tissues, system, and the intrinsic dysfunction of the regenerative mechanism.
In summary, the present chapter reveals that gut microbiota and a correct use of probiotic may play important pleiotropic functions on several levels and systems. It is now clear that there is a bidirectional interaction between microbiota and nervous system, microbiota and immunity, microbiota and bones, and eventually microbiota and mitochondria. Probiotics are getting more and more attention due to the increase of evidence of their benefits in many degenerative disorders. It shows the capacity of microbiota to restore gut and vaginal and oral microbiota, thus attenuating various severe inflammatory responses. All these findings suggest that probiotics could play a role in clinical procedure and therapy approaches to decrease the risk of morbidity and mortality related to CNS diseases, cardiovascular diseases, and bone degenerations. The shared information presented on this chapter may also demonstrate that the traditional view on gut microbiota and microbiome has changed and may be eventually useful as a prospective medium for the delivery of superior, more precise, and personalized treatments in the achievement of better protective health benefits for a more and more aging society.
The authors declare no conflicts of interest.
The authors contributed equally to this work.
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