Main agricultural conditions of selected countries

Abstract

The study analyses agricultural conditions of selected countries, namely Brazil, China, Egypt, Russia Federation, Turkey, Ukraine for the period of 2010-2018. Motivation/Background: The selected countries have important role in the world agricultural industry, therefore their developed level in this sector can make considerable influences. Also, developed level of agriculture can create more competitiveness for itself on the world market against the possible market competitors. The method of study is the special statistical program for social sciences. Results: Study focuses on the GDP growth, gross fixed capital formation, agricultural value added and also per annual working unit, foreign direct investments inflow into agriculture, irrigated lands, exchange rates, subsidies of general government and also environmental protection including enteric fermentation emissions and manure management emissions in carbon dioxide equivalent. The main issue is that the agricultural industry can provide enough livelihood for agricultural workers by the general government expenditures with foreign investments, while the aim of environmental protection can be realised or not. Also, the study compares the agricultural conditions of selected countries based on the above-mentioned main issue. The other issue is developed level of agricultural industry in each country. Conclusions: Subsidies of general government and foreign direct investments can stimulate to increase gross fixed capital formation in direction to more developed level of agriculture and agricultural value added for more adequate livelihood of workers of agriculture. The increase of agricultural competitiveness can ensure more market positions of these countries in the world market.

Keywords: agricultural value added, competitiveness, environmental protection, foreign direct investments, statistical analyse, subsidies of general government

Introduction

The study analyses agricultural conditions of selected countries, namely Brazil, China, Egypt, Russia Federation, Turkey, Ukraine for the period of 2010-2018. The motivation and background of the study are that the selected countries have important role in the world agricultural industry, therefore their developed level in this sector can make considerable influences. Also, developed level of agriculture can create more competitiveness for itself on the world market against the possible market competitors.
For the recent decades China has become leading economy at the world-wide side from point of view of heavy industry, mechanization, infrastructure including the project of One Belt and One Road from 2013, ambition development agricultural industry by considerable increase of governmental expenditure and environmental conservation. Russia Federation extends its agricultural development for food production and food export mostly for developing economies. In spite that Russia has very large amount of water resources by its rivers, but the cold climatic conditions, mostly in Asian areas, are not relevant to the enough agricultural production. At this present time Ukraine has les developing trends in agricultural production comparably to other selected countries, but this country has more potential capacity, natural resources for agricultural development, than this country uses. The most sensitive natural conditions are for agricultural development in Egypt, because this country has longest drought period, and most of area is desert, and Nile river is mostly only alone adequate water resource for the agricultural production. Comparably to the other selected countries Brazil has the most amount of water resource either for agricultural production or water energy production. But the large distant and relatively low density of population make difficulties for agricultural production and for creating network and logistic background for agricultural industry. Turkey has a little middle position from point of view of geographic conditions, because this country has more water resources for agricultural production than in Egypt, but considerably less than in Brazil.

These selected six countries of four continents can provide considerable comparing possibility among them from point of view their developed levels of agricultural industry and agricultural value added even per worker.

The data base can be used from the macro indicator given by FAO (2020) and some national statistical offices for providing national data for Foreign Direct Investment (FDI), Land area, Land area equipped for irrigation, Exchange rates, Agriculture value added per worker (US$, 2010 prices), Employment-to-population ratio, rural areas, Research & Development Agriculture, forestry, fishing (General Government), Government expenditure for agriculture, Enteric Fermentation, Emissions and Manure Management, Emissions (CO2eq) for the period of 2010-2018.

This analyse has five hypotheses, which are as follows:

1. The analyse enteric fermentation, emissions (CO2eq) (Ferment11), which very strongly accompanies with manure management, emissions (CO2eq) from CH4 (Methane) and N2O, in gigagrams (ManureEm12) between 2010-2018.
2. The exchange rates – annual, standard local currency units per USD in 2010 (ExcRate107) has very strong correlation with exchange rates – annual in 2019 (ExcRate198).
3. The manure management, emissions (CO2eq) from CH4 (Methane) and N2O (ManureEm12) has very strong contradict correlation with the agriculture value added per worker between 2010-2019 (AgrVaAdWo13).
4. Gross fixed capital formation, between 2010 – 2017 (GrossFCF2) has very strong correlation with land area, share of land area equipped for irrigation in 2018 in all of land area (LandEqIr186).
5. GDP growth per capita (GDPcap1) has very strong correlations with gross fixed capital formation, between 2010 – 2017 (GrossFCF2).
6. Share of FDI (Foreign Direct Investment) inflows to agriculture, forestry in all of the FDI in 2010 (FDItoAgr4) has important correlation with expenditure of general government for agriculture, forestry, fishing in 2010-2016 (AgrGenGov9) at level of 2010 price.

The study also focuses on environmental conservation and financing for sustainable economic growth and agricultural development from side both of expenditure of national general governments and the international side as FDI. For example, as National Agricultural Innovation Agenda, the Australian Government is providing national leadership and driving improvements across the agricultural innovation system by targeting Pillars of Reform the strengthening ecosystem leadership, cohesion and culture through clear strategic direction and increased collaboration and improving the balance of funding and investment to deliver both incremental and transformational innovation, and growing private sector and international investment (AG, 2020). In case of Russia Federation the modern trajectory of economic dynamics of the agricultural sector is characterized by uneven development of the agricultural complex (30% of agriculture is backward), high dependence of a number of agricultural sectors on foreign technologies and innovations (in some cases approaching 100%), depopulation of most ru­ral areas with pronounced negative social trends (in some subjects of the Russian Federation, the share of depopulated villages exceeded 20%) (Anokhina, 2020).

In agricultural industry if wages increase due to greater agricultural productivity, factories producing tradable goods, which are assumed to be operated by external producers, will move to avoid the higher wages (Lentner et al, 2019). Also, the increase the risk of creating poverty traps for beneficiaries, especially due to the impact of employment in informal sector (Mishchuk et al., 2020) therefore, the risk level should be decreased. The measure of dynamic efficiency can be used to analyse the performance of businesses in regards of inter-temporal optimization of the investment behaviour (Morris et al, 2019), which can reflect the investment behaviour of FDI. The economic and financial risk sources in SMEs of the V4 (Visegrád Group: Czech Republic, Hungary, Poland and Slovakia) and Serbia has been investigated in the context of the business environment, even in the agricultural sector (Oláh et al, 2019). Peters et al. (2020) performed analyses regarding the relationship between product decision-making information systems, real-time big data analytics, and deep learning-enabled smart process planning.

Also, the FDI is important for active changes in the social and economic formations, entry of foreign investors into the Ukrainian market, and emigration processes result in rapid transformations of the labour relations organization at the enterprises of our country (Yakubiv-Poliuk, 2019; Zhu et al, 2018). The land has important role for agriculture even by land equipped for irrigation. When conducting a ranking of the productive lands of the world, it becomes obvious that Russia is among the main leaders in the supply of arable land and forests in both absolute and relative terms. The analysis performed allows concluding that Russia occupies one of the leading places on a global scale in three main indicators: total arable land area, ploughed area (the territory of Russia is the least ploughed), arable land per capita (Voronkova et al, 2018).

Materials and methods

In order to analyse differences of agricultural conditions and some environmental conditions concerning this sector among the selected countries the SPSS (statistical program for social sciences) provide the best adequate methods (McCormick et al, 2017; IDRESC, 2020). The economic agricultural features of countries are used in this study are basic coming from the FAO and FAOSTAT statistical data mostly in period of 2010-2017. The first basic indicator is GDP growth per capita (GDPcap1), agricultural value added for the agriculture, forestry and fishing (AgrVA3) in annual growth rate and also the agriculture value added per worker (AgrVaAdWo13). These economic growing indicators are connecting with the gross fixed capital formation (GrossFCF2) providing assets for the economic prosperity. Basic asset of the agricultural production is land, which connects with irrigation investment as land area, share of land area equipped for irrigation in 2010 (LandEqIr105) and in 2018 (LandEqIr186) (Table 1; FAOSTAT, 2020, Macro Indicators).

The economic activities and performance need for financial resources, which can be provided either from FDI (Foreign Direct Investment) inflows to agriculture and forestry in all of the FDI (FDItoAgr4) or from expenditure of general government for this sector (AgrGenGov9). The financial issue also includes exchange rates – annual, standard local currency units per USD in 2010 (ExcRate107) and exchange rates in 2019 (ExcRate198). The national currencies given in this study are Brazilian Real, Yuan Renminbi, Egyptian Pound, Russian Ruble, Turkish Lira and Ukrainian Hryvni.

Also, the expenditure of general government can provide financial resources for the environmental protection (EnvProt0710) additionally to any production cost of firms, which can partly contribute to the expenditure of environmental conservation. In this study analyses focus on some parts of the gas emission as enteric fermentation emissions carbon dioxide equivalent (CO2eq) from CH4 (Methane) in gigagrams (Ferment11) and also other kinds of gas emission as manure management emissions carbon dioxide equivalent (CO2eq) from CH4 (Methane) and N2O, in gigagrams (ManureEm12). The gas emission issues connect with environmental conservation for interest of mitigating gas emission.

The economic indicators are economic variables as economic features of th selected countries in this study within the SPSS statistical analysing system. The agricultural development of these countries needs for using assets including gross fixed capital formation, land, irrigation investment and financial resources provided to ensure production process concerning the environmental conservation. Additionally, to private capital the financial capacity of central national general government is needed for economic prosperity and environmental conservation. Because these expenditures are so high, therefore the private capital and private companies cannot ensure enough financial resources for covering all their expenditure.

The statistical analyse includes the Pearson Correlations among economic variables (Table 2; FAOSTAT, Macro indicators) and Dendrogram using Ward Linkage. The Pearson Correlations show the measure of the correlations and their strengthen among the economic variables as economic features of selected countries. If the value of the correlations among variables are more than 0,800 (80%) these are very strong, and if their correlations are less level of 0.800 (80%) until 0.500 (50%), these are strong correlations. But under level of 0.500, the correlations are weak. If the value of correlations is negative, this means that the correlations among variables are contradict. This means that one variable increases the other one decreases in this analyse adequate for these selected countries in the same period.

Dendrogram using Ward Linkage, which in Figure 1 (FAOSTAT, 2020), shows the clustering the selected countries based on their economic variables as features. The Pearson Correlations show only the measure of the correlations of economic variables among selected countries, but the Dendrogram shows the clustering as classification for the countries. Each cluster includes those countries, which have similar economic variables or features to each-others and their economic variables of countries of one given cluster are more different from variables of other clusters including other countries. In this study three clusters were given, but the SPSS generally suggests maximum five clusters.

Statistical analysis and graphical presentation

In order to see clearly the correlations of economic variables in selected countries the data base should be overviewed accompanying with Pearson Correlation Matrix. The Table 1 shows the complex data base coming from the FAOSTAT (2020) concerning the Macro indicators, share of FDI inflow to agriculture, irrigated lands, exchange rate, general government expenditure, environmental protection, fermentation emissions and manure emissions Carbon Dioxide equivalent and finally, the agriculture value added per worker in percent between 2010-2018. The Table 2: Pearson Correlation Matrix shows the correlations among economic variables as economic features and Figure-1: Dendrogram using Ward Linkage shows the clustering six countries into classes based on their similar or different economic features.

Figure 1: Dendrogram using Ward Linkage
Source: Owned calculation based on the SPSS and FAOSTAT, 2020

The correlations among economic variables can determine later on the clustering selected countries, namely Cluster-1: Egypt, Ukraine, Brazil, Cluster-2: Russia Federation and Cluster-3: China, Turkey

Results and discussion

Additionally, to the data base, the Table 2: Pearson Correlation Matrix exactly shows the correlations among economic variables, which even can determine later on the clustering selected countries, therefore the first step is to calculate correlations (Figure 1, FAOSTAT, 202). The very strong correlations are between enteric fermentation emissions (CO2eq) (Ferment11), and manure management emissions (CO2eq) (ManureEm12) by value of 0.992 (99.2%), which means that when enteric fermentation emissions carbon dioxide equality (CO2eq) increase, also, manure management emissions increase relevant to the economic and agricultural prosperity. The other very strong correlations are between exchange rates – annual, standard local currency units per USD by 0.972 in 2010 (ExcRate107) and in 2019 (ExcRate198), which means that consequently rates of national currencies of selected countries have sharply devaluated comparably to the US dollar for the period of 2010-2019. For example, Russian Ruble standard local currency units per USD was 30.3679 in 2010 and then this was 64.7377 Russian Ruble by the end of 2019, which means that this currency devaluated by two time more within one decade comparably to US dollar. Hryvnia, Ukrainian currency has devaluated mostly by three times more from 7.9 to 25.8 per US dollar. Only the Yuan Renminbi of China has similarly been at level of between 6.7 and 6.9 Yuan per US dollar. This means that the imported products to selected countries – except China – have become more expensive for the same period (FAOSTAT, 2020, Exchange rates).

Also, there are very strong correlations between manure emissions (CO2eq) (ManureEm12) and agriculture value added per worker (AgrVaAdWo13) by value of -0.899, which means that the correlations are contradict. This means that if the agriculture value added per worker increases the other one decreases, because the production technology can develop, therefore the manure emission can decrease, while the agriculture value added per worker can increase. This correlation is not general in practice, just only this is true for cases of these selected fixed countries in the same period. Also, there are the other very strong similarly contradict correlations between the agriculture value added per worker (AgrVaAdWo13) and enteric fermentation emissions (CO2eq) (Ferment11) by value of -0.877. This also means that when the agriculture value added per worker increases the enteric fermentation emissions decrease. In case of Russia Federation, the fermentation emissions have decreased from 37012 gigagrams to 33251 gigagrams for the period of 2010-2018 and manure emissions have decreased from 11023 gigagrams to 10950 gigagrams for the same period. Only in Turkey fermentation emissions have increased from 11407 gigagrams to 17309 gigagrams by 54% for the period of 2010-2018, while the manure emissions have also increased from 605 gigagrams to 903 gigagrams by 49% for the same period (FAOSTAT, 2020, enteric fermentation, emissions (CO2eq) 2010/2018). In Russia fermentation emission was by three times more than one of Turkey in 2010, while this was little less by two times in Russia than in Turkey by the end of 20218. This means that fermentation emissions of Turkey comparably were 31% of this one of Russia in 2010, but this share increased to 52% of fermentation of Russia in 2018, because fermentation increased in Turkey and decreased in Russia.

Gross fixed capital formation (GrossFCF2) and share of land area equipped for irrigation (LandEqIr186) have strong correlations by value of 0.895, but only in 2018, which means that the gross fixed capital formation includes the advanced equipped irrigation system, which was less developed in 2010 than in 2018. Therefore, the correlation between both of them, namely GrossFCF2 and LandEqIr105 was mostly weak only in value of 0.549 in 2010. Also, the gross fixed capital formation (GrossFCF2) has very strong correlations by 0.878 with GDP growth per capita (GDPcap1), which means that investment activities accompanying with innovation make considerable stimulating influences on the prosperity of GDP even per capita, as important effective-profitable development. This means that the selected countries mostly have ambition economic growth trends. Also, GDPcap1 has very strong correlations with share of land area equipped for irrigation (LandEqIr186) by value of 0.848, because this last one is included gross fixed capital formation, if the gross fixed capital formation can stimulate increasing of GDP growth per capita (GDPcap1), therefore one important element of GrossFCF2 can effect on increase of GDP growth per capita in cases of all of the selected countries in 2018.
The under tables can show the exact similar and different features of selected countries in some economic fields. The Table 3: Gross fixed capital formation at the national economic level, for period of 2010-2017 makes clearly that gross fixed capital formation has only decreased by 17% in Brazil from 453578 to 375338 million US dollar. This means that the economic backwardness increased in this country, in spite that the gross fixed capital formation was just only little less in value than in this filed in Russia. The gross fixed capital formation could decrease in Brazil by reason of decreasing FDI and less profitable fixed capital formation. This negative trend could occur in spite that Brazil has become one of the biggest crude oil producer and exporter in the world economy. In spite that Turkey has achieved the highest-level ambition prosperity by 83% in field of gross fixed capital formation, Turkey has little back from the level of Brazil in this field by the end of 2017. Based on data of Table 3, China had 72% share of all selected countries in field of gross fixed capital formation, which has increased to 79% as share of the selected countries in the same economic field. The gross fixed capital formation is needed for the prosperity of infrastructure and advanced mechanization, which are at highest level in China opposite to the other selected countries.

The lowest level of gross fixed capital formation was in Ukraine, which has only increased by 2.4% for the same period as less than half of one of Egypt. In this case Ukraine has mostly stagnating level and its developing trend in fields of infrastructure and mechanization has very backwardness even under the level of Egypt. This backwardness is very consequent in spite that Ukraine has favourable geographic conditions for development of either industry or agricultural sector.

Table 3: Gross fixed capital formation
(GrossFCF2), in 2010-2017, 2010 prices, million US Dollar
Countries 2010 2017 2010=100
Brazil 453578 375338 -17
China 2744755 4517090 65
Egypt 41236 53443 30
Russia 357976 380743 6.4
Turkey 191951 351895 83
Ukraine 23215 23773 2.4

Source: FAOSTAT, Macro indicators

Table 4: Agricultural Value Added
(AgrVA3) (Agriculture, Forestry and Fishing) in Annual growth rate in percent between 2010-2018, US Dollar at 2010 price
Countries 2010 2017 2010= 100
Brazil 90910 99919 10
China 598647 790414 32
Egypt 28632 35234 23
Russia 51004 65616 29
Turkey 69670 84711 22
Ukraine 10130 13305 31

Source: FAOSTAT, Macro indicators

The Table 4 shows the data for agricultural value added of agriculture, forestry and fishing in annual growth rate in percent between 2010-2018. In case of agricultural value-added China has also achieved the highest level in field of prosperity for this field by 32%, while Ukraine has the worst position in this field even by second highest fast development by 31% after China. China had 70,5% as share of selected six countries in this field in 2010, while this was 72,6% by the end of 2018. This share of China was similar to share in field of gross fixed capital formation of all selected countries. This means that China had some forces in agricultural value added of agriculture than in gross fixed capital formation. China could apply the advanced technology in industry as same as in agriculture. Naturally the Agricultural industrial development needs first for the mechanization and second irrigation system.
The Table 5 shows the agriculture value added per worker, in which of field Ukraine has more favourable conditions, because the highest fast development was in this field of Ukraine by 129% for the researched period, but China was the second one by 101% after Ukraine. Russia, Brazil and Egypt have also implemented considerable prosperity by 67%-75%. In spite that Turkey had lowest moderate development by 32% in this field, but Turkey could reach the highest level by 17212 US dollar in field of agriculture value added per worker. Russia was the second one by 16379 US dollar and the third one was Brazil by 13843 US dollar by the end of 2019. Turkey and Russia, and somehow Brazil could realise a considerable prosperity in this field, because they could concentrate the basic agricultural production within family farms size or farming business companies.

Table 5: Agriculture value added per worker
(AgrVaAdWo13) (US$, 2010 prices) in US dollar between 2010-2019, 2010= 100
2010 2019 2019/2010
Brazil 8171 13843 69
China 2089 4191 101
Egypt 4130 6912 67.4
Russia 9361 16379 75
Turkey 13049 17212 32
Ukraine 2501 5733 129

Source: FAOSTAT, Employment Indicators

Also, in cases of these three countries the general government subsidies for agriculture, forestry and fishing (AgrGenGov9) for period of 2010-2016 and the share of FDI inflows to agriculture and forestry in all of the FDI in 2010 (FDItoAgr4) increased and was at highly level. The other selected countries, namely Egypt, Ukraine and China the basic agricultural production could not enough concentrate in order to increase agriculture value added per worker. This means that in these other three countries, the share of rural population from all national population is so highly accompanying with highly level of agricultural employment and not so much advanced mechanized level. In China the agricultural conditions are very contradict, because the gross fixed capital formation and agricultural value added have ambitionally developed for researched period, but these do not have considerable effect on the income conditions per worker based on the agriculture value added. In China most of the gross fixed capital formation expenditure was spent for the infrastructure and not enough mechanization.

Conclusions and recommendations

The enteric fermentation, emissions (CO2eq) (Ferment11) very strongly accompany with manure management, emissions (CO2eq) from CH4 (Methane) and N2O, in gigagrams (ManureEm12) between 2010-2018. The exchange rates – annual, standard local currency units per USD in 2010 (ExcRate107) has very strong correlation with exchange rates – annual in 2019 (ExcRate198). The manure management, emissions (CO2eq) from CH4 (Methane) and N2O (ManureEm12) has also very strong contradict correlation with the agriculture value added per worker between 2010-2019 (AgrVaAdWo13). Gross fixed capital formation, between 2010 – 2017 (GrossFCF2) has very strong correlation with land area, share of land area equipped for irrigation in 2018 in all of land area (LandEqIr186). GDP growth per capita (GDPcap1) has very strong correlations with gross fixed capital formation, between 2010 – 2017 (GrossFCF2). Share of FDI (Foreign Direct Investment) inflows to agriculture, forestry in all of the FDI in 2010 (FDItoAgr4) has important correlation with expenditure of general government for agriculture, forestry, fishing in 2010-2016 (AgrGenGov9) at level of 2010 price.

Based on the clustering system China and Turkey are closed to each other, first in field of gross fixed capital formation, where they have considerable growth than the other selected countries. Brazil, Ukraine and Egypt are closed to each other, because Egypt has lowest level growth and Brazil has somehow decline in field of gross fixed capital formation, but the agricultural value added middle higher level increased in Egypt and Ukraine. While the agricultural value added per worker increased highly in Ukraine and considerably in Egypt and Brazil. Averagely these three countries classified into one cluster. Russia Federation has a special growing condition in researched economic features, which put this country into middle independent cluster between the other two clusters.
China had 72% share of all selected countries in field of gross fixed capital formation, which has increased to 79% as share of the selected countries in the same economic field. The gross fixed capital formation is based on the development of infrastructure and advanced mechanization, which are at highest level in China opposite to the other selected countries. Subsidies of general government and foreign direct investments can stimulate to increase gross fixed capital formation in direction to more developed level of agriculture and agricultural value added for more adequate livelihood of workers of agriculture. The increase of agricultural competitiveness can ensure more market positions of these countries in the world market.

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Sándor J. Zsarnóczai
Institute of Environmental Engineering, Rejtô Sándor Faculty of Light Industry and Environmental Protecting Engineering, Óbuda University, Budapest, Hungary

Norbert Somogyi
Faculty of Agricultural Sciences, Szeged Scientific University, Hódmezôvásárhely, Hungary