The study analyses reasons of differences of agricultural industry in United States, Russia Federation, China and Japan in fields of agricultural strategy and policy, namely general government subsidies to develop the agricultural industry and mechanization in period of 2010 and 2018. Overview the investment activities of these countries for applying the highly developed technology of agriculture to cover domestic food demands and possibly agricultural export. Motivation/Background: Selected countries have important ambition agricultural development, which can lead to increase their food and agricultural product export for the world market and their influences on the world market price of these products. Method: Use the comparing statistical system by statistical analyses for features of their agricultural sector. Results: In China the general government provided 237647 million US dollar in 2016, which has increased by 97,9% since 2010, while Russia Federation provided only 7240 US dollar for agricultural industry, decreased by 23% from 2010. Analyse the reasons and influences of less subsidies for agriculture in Russia, than in China. Also, the less irrigated lands and less favourable exchange rate of Russia could contribute to the less increase of agricultural value added per worker by 75% than in China by 101% for the same period. In 2019 in US the government expenditure for agricultural sector was enough for creating the agricultural gross value added per worker by 20 times more than in China. General Government subsidies of Japan were consequently considerable and these were 53396 million US dollar by the end of 2019, which have increased only by 1.7%, but seven times more than once of Russia. Conclusions: The future trend of agricultural prosperity should follow the more dynamic development in mechanization and more advanced value added exported agricultural products instead of partly manufactured or basic products of this sector in these countries.
The study analyses reasons of differences of agricultural industry in United States (US), Russia Federation, China and Japan in fields of the agricultural strategy and policy from point of view of mechanization and general government subsidies to develop the agricultural industry in the period of 2010 and 2018. Overview the investment activities of these countries for applying the highly developed technology of agriculture to cover domestic food demands and possibly agricultural export. The motivation and background of the study is that US, Russia, Japan and China have important ambition agricultural development, which can lead to increase their food and agricultural product export for the world market and their influences on the world market price of these products. In this study additionally to China and Russia Federation the other two countries namely United States (US) and Japan are included, because only two countries for analysing are not enough to compare some main economic features and to overview the role and economic impact of China and Russia on the world economic conditions. Actually, these four countries can determine the main prosperity of agricultural industry by advanced mechanization and adequate capital supply for the agricultural production within the gross fixed capital formation. The foreign direct investments (FDI) as capital flow into national agricultural sector accompany with profit interests of international corporations by investments realised in direction to agricultural prosperity at national wide side level. The expansion and international applying of the advanced technology, as aims of the FDI are common interests with China, therefore China stimulates the FDI inflow to national agricultural industry. China has widder opened the door of its national economy for FDI comparably with other selected countries. In this study the analyse concerns the FDI inflow into agricultural sector of China.
Also, the motives of Chinese agricultural FDI are affected by corporate goals, national strategies, and the international environment. For China, overseas agricultural investment guarantees national food security, helps expand the agricultural product market, and enhances China’s influence on agriculture of other countries. China’s agricultural investment links are transformed from planting to full-industry chain operations (Jiang et al, 2019).
The hypotheses can be summarised in cases of four countries, which are as follows:
a. There are very strong correlations between GDP growth per capita (GDPcap1) and general government payment for agricultural sector (AgrGenGov9).
b. There are very strong correlations by 0.963 (96.3%) between exchange rates by annual, standard local currency units per USD in 2010 and in 2019 (ExcRate107 and ExcRate198).
c. Gross fixed capital formation (GrossFCF2) has very strong correlations with increasing trends of General Government payments (AgrGenGov9).
d. The GDP growth per capita (GDPcap1) can be stimulated by developing and increasing gross fixed capital formation (GrossFCF2) by very strong correlations.
e. The agricultural value added has naturally strong correlations with agricultural value added per worker. The share of land area equipped for irrigation in 2018 in all of land area in %, (LandEqIr186) and exchange rates (ExcRate107) in 2010 have very strong correlations.
The national economic background of four largest economies of the world economy is based on the financial resources, capital assets, national market conditions and advanced technology with the highly developed educated corporation governance and management. Also, the land grabbing used by the international corporations to ensure their food and energy interests and to realise the agricultural prosperity requirements in other regions of the world economy. Often the land grabbing is equally with land private ownership realised by international corporations.
Some experts emphasized the importance of the developing agricultural industry for workers employed in this sector from the local and regional population to increase agricultural productivity, which can be a real base for wages of agricultural workers by increasing the competitive positions for agricultural products either at national or international markets (Lentner et al, 2019).
In analyse of objectives of the study the comparing statistical system is used based on the statistical analyses for features of agricultural sector in China, United States, Russia Federation and Japan. Therefore, the SPSS (Statistical Program for Social Sciences) as statistical analysing system provides an adequate possibility for knowing the correlations among the economic features, as economic variables according to the selected countries. The economic variables are set up on the Pearson Correlations, which are as GDPcap1, GDP growth per capita; GrossFCF2, as gross fixed capital formation; AgrVA3, as agricultural value added in annual growth rate; AgrVaAdWo4, as agriculture value added per worker; FDItoAgr5, as share of FDI (Foreign Direct Investment) inflows to agriculture, forestry in all of the FDI in 2010; LandEqIr186, as land area, share of land area equipped for irrigation in 2018 in all of land area; ExcRate107, as exchange rates – annual, standard local currency units per USD in 2010; ExcRate198, as exchange rates – annual, standard local currency units per USD in 2019 in national currencies, as Yuan Renminbi, Russian Rubel, Yen comparably to the US dollar; AgrGenGov9, agriculture, forestry, fishing (general government). The SPSS statistical analysing system was worked out international studies from IDRESC (Institutional for Digital Research and Education Statistical Consulting, 2020; McCormick et al, 2017).
The data base was used from main interesting sources FAOSTAT, Macro indicators, Employment Indicators, Foreign Direct Investment (FDI) Government and also World Bank data,2020. Also, some data comes from US data base as United States Department of Agriculture, Washington.
The study analyses the main economic variables and their corelations in cases of four selected countries mentioned above. The Table-1 summarised the statistical data concerning the economic prosperity of these countries based on their special economic features. The US as the strongest capital investment country could realise 32.6% increase in field of gross fixed capital formation, between 2010 – 2017, even this result was second after the prosperity level of China by 65%. In spite the Chinese development in this field was higher the result of US, but US could implement higher qualified development trend in mechanisation, innovation, issuing highly developed production technologies. At present US remains in the first position in field of top highly developed technologies issuing country in the world economy, because most of these technologies created by US transnational corporations and they supply demands of majority of the highly developed economies by these technologies for. Based on the top highly developed technologies the US could consequently increase GDP growth by averagely annual 15%, which even the second after 84% increase in field of GDP growth per capita in China. The economic prosperity of China realised the top development trend in development trend, which need for more innovation increase to decrease some backwardness from the developed level of US (Table 1; FAOSTAT, Macro indicators; World Bank data,2020).
The Table 2 shows the Person Correlations among economic variables, which are determined by the economic features of four countries, China, Russia, Japan and United States. If values of correlations among economic variables are more than level of 0.800, as 80%, the correlations are very strong. But if values of correlations among economic variables are between levels of 0.500 and 0.800, as 50% and 80%, the correlations are strong. The correlations among variables are weak from the level of 0.500 as 50%, therefore these are not important for analysing. When one economic variable is negative, this means that this variable is contradict or in inverse ratio to changes of the other variables. In this study the negative correlations are for cases of two variables, namely the agricultural value added and agricultural value added per worker, which can be followed in the Table 2. But these two negative economic variables change in the same trend and not contradict terms.
In Table 2 the very strong correlations are between GDP growth per capita (GDPcap1) and general government payment for agricultural sector (AgrGenGov9) by value of 0.979 (97.9%) in cases of four countries. This means that if the general government payment increases for the agriculture, the GDP growth per capita also increases. This also means that the profitability and efficiency of the agricultural sector increase by increasing central governmental subsidies, which stimulate for increasing GDP growth per capita.
There are very strong correlations by 0.963 (96.3%) between exchange rates by annual, standard local currency units per USD either in 2010 or in 2019 (ExcRate107 and ExcRate198). This means, when any country from other three countries has a kind of exchange rates by annual, standard local currency per US dollar in 2010, mostly these exchange rates remained by the end of 2018. Gross fixed capital formation (GrossFCF2) has very strong correlations by 0.959 (95.9%) with increasing trends of General Government payments (AgrGenGov9). When the subsidies coming from the General Government increase to develop the fixed capital formation, this stimulates production processes and their incomes either in agricultural sector or in the other sectors. Also, GDP growth per capita (GDPcap1) can be stimulated by developing and increasing gross fixed capital formation (GrossFCF2), therefore they have very strong correlations by value of 0.916 (91.6%), (FAOSTAT, 2020; World Bank, 2020). The agricultural value added has naturally strong correlations with agricultural value added per worker by 0.896 (89.6%), because when the first one increases the other one per worker also can increase.
Also, share of land area equipped for irrigation in 2018 in all of land area in %, (LandEqIr186) and exchange rates (ExcRate107) in 2010 have very strong correlations by 0.882. The share of land area equipped for irrigation (LandEqIr186) has strong correlation by 0.724 with exchange rates USD in 2019 (ExcRate198). In spite that these correlations are not very strong as the others’ one, but these correlations are also strong. These correlations could be less than level of the “very strong” in 2019 comparably to the conditions in 2010. The reason of differences between conditions of 2010 and 2019 was the decline of the exchange rates, mostly in Russia and in Japan by decreasing value of national currencies comparably to US dollar. But this exchange rate was going wrong because of general economic production and other economic sectors, not essentially because of the agricultural production.
Also, the agricultural production generally increased even by the developing irrigation system in Russia and Japan, but the price level of input increased mostly because of mechanization and modernisation finally the innovation, which mainly came from increasing imported production technologies. The innovation is also costly in agricultural sector. Therefore, the more expensive imported technologies could contribute to the less favourable income positions of basic agricultural production and exchange rate comparably to US dollar by the end of 2019 in Russia and Japan. Price of inputs increased more than the price of output in agricultural sector mostly in Russia and partly in Japan. In case of China the exchange rate of national currency has consequently been fixed comparably to US dollar for researched period.
Two economic variables namely the agricultural value added (Minus-AgrVA3) and the share of land area equipped for irrigation by the end of 2018 (LandEqIr186) have had very strong contradict correlations by value of (Minus) 0.886 for the researched period. This means that when the irrigation system was developed by the end of 2018, which could effect on the decreasing agricultural value added. Also, the agricultural value added increased, when the share of land area equipped for irrigation decreased by the end of 2018. The general issue was that the developing irrigation system could not make considerable increase in basic agricultural production, because the share of irrigated lands was not considerable, therefore the irrigation system did not considerably effect on increase of the agricultural value added at sector level in selected countries. Naturally individually in some cases the agricultural production could increase on each farm including irrigation development. This difficulty has been resulted by less capital investment on most of farms and lands used accompanying with lack of capital, mainly in Russia and China, but partly as well as in Japan for the researched period. The global warming created longer drought period, which could not be balanced by more irrigation investment, because of lack of capital of farms usually was. When the agricultural value added increased, mostly the possible more agricultural income was spent first for mechanization and not for the developing irrigation system. Generally, the FDI inflow into the agricultural industry is very weak activity, because the foreign investors need for obtaining and acquiring lands in other countries, namely in Russia and China, partly in Japan, but the national economic interest does not allow land private ownership for foreign investors.
The agricultural value added (Minus-AgrVA3) and exchange rates – annual, standard local national currency units per USD in 2010 (ExcRate107) by (Minus) 0.820. This contradict very strong correlation can be explained that the increase of agricultural value added could not make favourable exchange rates of national currencies per US dollar, because the role of the basic agricultural production, food manufacturing industry in GDP and agricultural export-import are generally weak. Also, the wronging exchange rates of Russia and China, sometimes in Japan did not stimulate to purchase the expensive imported agricultural machines and equipment aiming at agricultural development accompanying with the increasing agricultural value added. The agricultural and food products of Russia and China are not highly qualified and developed products; therefore, the export incomes can be at lower level, which do not strengthen the national currencies against US dollar. Also, producers and traders of both of countries should sell their national agricultural products on the world market at lower price in order that these one could be sold.
GDP growth per capita (GDPcap1) and agriculture value added per worker (AgrVaAdWo4) have strong correlations by value of 0.732, which means that if the GDP growth per capita increases also, the agriculture value added per worker (AgrVaAdWo4) increases. In United States the GDP growth per capita increase has been as same as increase of agricultural gross value-added growth for the researched period. This means, that generally, subsidies for agricultural production increased as increase of GDP growth in US, therefore, both of them could increase at the same rate. In China the GDP growth per capita was 84% as top level in cases of four countries and increase of agricultural gross value-added growth was 101%. In China increase of this last one could realize based on the considerable subsidies for agricultural basic production, which was significant comparably at world wide-side, because the Chinese agricultural strategy aims at wholly supplying for food demand of domestic market from domestic internal resources by less import of these products. This aim is considerable, because the Chinese domestic market is the largest one in all of the world economy. In Russia in spite that the GDP growth was moderate by 12%, but the agriculture value added per worker considerably increased by 75%, which means that the agricultural subsidies and production growth were at highest level comparably to GDP growth per capita within four countries. The aim of Russia is to increase the food production and export as much as this country could, because Russia would like to supply more food demand of more developing countries, as food export leading country of the world market. There is an interesting contradiction between two counties, because one aims at supplying domestic market and the other one aims at supplying world market.
The Figure 1 shows the classification of selected countries based on their economic features concerning the Dendrogram using Ward Linkage, as Rescaled Distance cluster Combine within the SPSS statistical analysing system. The Dendrogram system demonstrates that the US and Russian Federation are some-how closed to each other by their variables and economic conditions, because in Russia and US the large farming system is operating with relatively highly advanced mechanization, naturally advanced technology of US is more developed than in case of Russia. While China and Japan are independent, which are classified into one-one different classes in cases of three clusters.
The main issue for the economic development for any country is that for how long time the top highest developed technologies can have been invested into the general civil economic growth and performance. In US there is a realised consequent trend, that in every five-year period could change the top technology, which can start to be invested into the general civil economic growth by the transnational corporations. In case of Russia Federation and earlier Soviet regime changing period of the top highly developed technologies was mostly every seventeen-year period, which was long time ensuring the consequent relative economic backwardness in its performance from the developed level of US. This economic and technological backwardness can be followed by the lower qualified level of every day used technical equipment at level of households and consuming product structure. Also, the lower level of purchase power parity of consumers in Russia contribute that households are less developed by consuming products.
In spite that China realised internationally wide side very considerable GDP growth rate per capita within the researched period, because in 2010 the GDP per capita in China was about 9.2% of per capita in GDP of US, and the GDP per capita was 4487 US dollar, while this was 48574 US dollar per capita in US. In 2019 in China the GDP per capita was 8242 US dollar, which mostly increased by two times more by the end of this year, which was about 15% comparably to US dollar per capita in US. Only GDP per capita in Japan was closed to GDP per capita in US by 49188 US dollar, which was 88% of GDP per capita in US. This means that by the end of 2019 the GDP per capita in Russia and China all together was only 41% of GDP per capita in Japan (Table 3; FAOSTAT, Macro indicators). The service sectors and wide-side production structure of Japan can ensure an adequate internationally accepted standard of living for its population in spite that the food price level is very extremely highly comparably to food price level of the world market. Also, in China the food supply and the food self-sufficiency are considerably higher than very much lower level of food products in Japan (FAOSTAT, Macro indicators; World Bank data,2020).
The highly level standard of living of population in Japan can have been covered by considerably lower level of price of general consuming products and food products imported from China for the latest decades. The general economic strategy of any country is to keep the highest level of food self-sufficiency as much as it can be realised even by wholly food self-sufficiency.
According to Table 4 in Russia these economic conditions have been proofed by lowest level of increase by 6.4% in field of gross fixed capital formation (GrossFCF2) within four countries for the same time, which was mostly only 10% of Chinese increase in this field. Also, the GDP growth of Russia was 12%, in spite that this was higher than the 11% in Japan, while Japan could realise wide side export by highly value-added industrial products, and Russia mostly exported fossil energy resources and basic agricultural and food products. Naturally the export structure of US is more developed value-added products than the other three countries (Table 4; FAOSTAT, Macro indicators, 2020).
Japan can permanently purchase the developed technology, licence and know-how mostly from US and can ensure cheap consuming industrial products for consumers in US and European Union and also some markets of developing countries. But Japan is very vulnerable economy from point of view of lack of fossil energy resources and arable land, which make Japan be net importer of energy resources and food products. Therefore, Japan should become exporter of consuming industrial products to cover the highly price level imported energy and food products. The other three countries have favourable conditions in the energy resources and food productions.
The main issue is in case of China that the very considerable investment activities including “One Belt and One Road Projects” can withdrawal capital investment capacity from the home-national performance and therefore, how benefits can be realised from the foreign direct investments realised by Chinese companies abroad or in other countries or continents.
According to the Table 5, in China the agricultural value added (AgrVA3) has been growing by 32% for the period of 2010-2017 and at 2010 price level, therefore this was 790414 million US dollar in 2017 based on the considerable annual growth rate (FAOSTAT, Macro indicators). This was the highest level in four countries, in inverse ratio to the agricultural value added of US, because in US the agricultural value added was 168256 million US dollar by the end of 2017, which was about 21.3% of Chinese agricultural value added. All these other three countries reached 283102 in field of the agricultural value added, which was 35.8% of agricultural value added of China, as very considerable positive result of China in order to follow the possibly highest food self-sufficient.
But as Table 6 shows that in China the agriculture value added per worker (AgrVaAdWo4) (US$, 2010 prices) was only 4191 US dollar comparably to 84871 US dollar in US by the end of 2019, in which this value of China was about only 5% in value of US per worker. This means that in China 20 agricultural workers produced that agricultural value added produced by one American agricultural worker in US. Therefore, in US the efficiency of agricultural production per worker was 20 times more than in China. Also, in Japan agriculture value added per worker was equal with 28,5% of same value in US, which means that about 3 or 4 farmers of Japan produced agricultural value per worker in US. Therefore, the agricultural production can be more efficient by 3.5 times more than in Japan and 5 times more than in Russia, because in Russia one agricultural worker produced 19,3% of per US agricultural worker.
Three countries, namely China, Russia and Japan had 44765 US per agriculture value added per worker all together, which was equally with 57.2% of US agriculture value added per worker. This considerable agricultural result of US shows how much the agricultural production should be concentrated within the farm system in US. The US could become the second biggest agricultural and food production exporter by agricultural innovation after the European Union on the world market.
Increase of the agricultural value added needs or innovation development, which includes main fields of innovation and emerging technologies as next-generation sequencing and other advanced biotechnologies; technologies for sustainable, circular, and organic agriculture; advanced precision agriculture technologies (unmanned aerial vehicles, sensor networks, swarm robotics, artificial intelligence); equipment for urban agriculture (recirculating aquaculture, vertical farms); advanced waste utilization technologies, including next-generation bioenergy; smart agro-logistics, robotic storage, and transportation systems; technologies for the production of highly personalized and functional food; technologies for the production of synthetic and tissue-engineered food (Gokhberg – Kuzminov, 2017; Sycheva et al, 2017). Also some experts declared that it is worth noting that the flow of industrial capital into agriculture can occur not only through integration schemes but also in other ways: from enterprises – processors of agricultural products on the basis of long-term contracts for the supply of agricultural products, from agro-service companies to provide the production facilities, from leasing property of agricultural enterprises, through leasing, bank lending, mortgage of land and real estate (Ekimova et al., 2018; Voronkova et al, 2018).
In the United States, there are two main features, one is the agricultural production mostly goes within family farm system and the other one is that the agricultural technology has sharply increased and developed for the latest decades. Nearly 96% of the 2.2 million farms are family owned and operated. The USDA defines a family farm as “any farm organized as a sole proprietorship, partnership, or family corporation. Family farms exclude farms organized as non-family corporations or cooperatives, as well as farms with hired managers” (USDA, 2007). Using gross annual sales, the USDA further classifies family farms as:
There is the other way for determining the farm sizes based on their land measures. The small family farms share 88 percent of the family-farms. According to the USDA in 2013, small family farms averagely have 231 acres (1 acre=0,406 hectare; 94 hectare). In 2012-2013 there were 2109,8 thousand farms, of which 88% was 1856,6 thousand farms and 174,5 million hectares large family farms average 1,421 acres (577 hectare) and the very large farm average acreage is 2,086 acres (847 hectare). The advanced agricultural technology can lead to the increasing productivity based on the production concentration in agricultural sector. The number of 300,000 small farms have decreased since 1979, which stimulated land use and ownership concentration. For example, the agricultural production in the US has increased by an average of 5% each year since 1990. In addition, the output of each agricultural worker has grown by an average of 0.84 percent each year. On average, one American farmer produces enough food for 96 people (Statista, 2021; Dunckel, 2013).
From this point of view of the production concentration of family farms system and agricultural technology the US has reached the best highly level of both of them in cases of the selected countries.
According to the Table 7 the FDI inflow into agriculture (FDItoAgr5) generally is not so considerable into agricultural industry in cases of selected countries, because this sector concerning the basic production needs for intensive capital investment even accompanying with highly developed mechanization. Also, the benefit and returns from this sector are at low level or need longer time in spite that the intensive capital investment with considerable consumption of fixed capital is implemented, which are demanded to keep the competitive positions against the international competitors. Sometimes the capital accumulation for innovation of the agricultural production is weak even in highly developed economies. Also, the infrastructure, logistic network, lack of capital of farmers and information flow and feedback can be weak mostly in Russia and China. Also, in these two countries the agro-business based on the connection of agricultural sector with other economic sectors, namely input production supplying machines and equipment for farmers and manufacturing sectors for food and agricultural basic products is weak. The transport-costs built in the market prices of agricultural and food products make final agricultural product prices be higher level. Additionally, to the above-mentioned difficulties the climatic and geographic conditions are vulnerable for the agricultural producers. All of these reasons create less favourable conditions for the FDI to realise investment into the agricultural sector. Often product channels are not quietly enough developed in all of the parts of the channel. Some law conditions are not favourable for foreign investors, for example the land ownership, which is only favourable for domestic-national agricultural farmers, companies and earlier national land owners. Mostly this last reason can avoid the willingness of foreign investors to invest into basic production of agricultural sector.
In case of US the FDI inflow to agricultural sector has considerably increased by 410% about four times more since 2010. Also, Japan has the second largest increase of FDI inflow into agricultural sector by 333% since 2015. In spite that FDI inflow into agriculture of China has increased by 25% since 2010, this FDI inflow was 2500 million US dollar, while the FDI inflow into agriculture in US was only 30% of FDI inflow in China. Probably the firm tax conditions can stimulate the FDI inflow activity in case of China. In Russia the FDI inflow into agriculture has drastically decreased by 80% since 2010, which was only little more than value of FDI inflow level in case of Japan. In Japan the agricultural production mostly is more expensive, because of the geographic conditions are less favourable than in cases of other selected countries, which keep back the FDI inflow into this country.
According to the Table 8, in China the general government (AgrGenGov9) provided 237647 million US dollar for agricultural sector in 2019, which has mostly doubly increased by 97.9% since 2010, while Russia Federation provided only 7240 US dollar for agricultural industry, decreased by 23% from 2010. Less subsidies for agriculture were in Russia, than in China. Also, the less irrigated lands and less favourable exchange rate of Russia could contribute to the less increase of agricultural value added per worker in US dollar than in US and Japan in the same period (Table 6; FAOSTAT, Government expenditure, 2020). In 2019 in US the government expenditure for agricultural sector was enough for creating the agricultural gross value added per worker by 20 times more efficient than in China in spite that in US the government expenditure for agricultural sector was 62,7% of Chinese one. This means that less subsidies were paid for agricultural workers in US than in China, but more efficient agricultural value added per worker in US than in China. General Government subsidies of Japan were consequently considerable and these were 53396 million US dollar by the end of 2019, which have increased only by 1.7%, but seven times more in value US dollar than the once of Russia for the same time.
In this part of the study, it was clear that China has achieved the largest positive prosperity result for GDP growth in the world economy and developing agricultural sector comparably with the other selected countries. This means that China has a possibility by considerable economic prosperity, as increasing GDP growth per capita, gross fixed capital formation, agricultural value added even per worker, FDI inflows to agriculture, forestry and fishing, general government subsidies, to reach the highly economic developed level of US performance after a time period.
There is an historical example, when Russia has started to develop its economic prosperity from the feudalism by discontinuing serfdom villeinage based on the emancipation of serf since 1861 and after one hundred year even within this time period, Russia became the second biggest economy after US within two polar world economy. This was true, in spite that since the end of 1980s Japan had 2000 billon US dollar GDP, while Russia (former Soviet regime) had about 1700 billion US dollar GDP and US had 4000 billion US dollar GDP in 1989 before the collapse of the Soviet regime (World Bank).
It can be declared that the five (a-e) hypotheses are accepted, because really there are very strong correlations between GDP growth per capita (GDPcap1) and general government payment for agricultural sector (AgrGenGov9) and very strong correlations by 0.963 (96.3%) are between exchange rates by annual, standard local currency units per USD in 2010 and in 2019 (ExcRate107 and ExcRate198). While the gross fixed capital formation (GrossFCF2) has also very strong correlations with general government payments (AgrGenGov9). It is true that the GDP growth per capita (GDPcap1) can be stimulated by increasing gross fixed capital formation (GrossFCF2) based on very strong correlations. The agricultural value added has strong correlations with agricultural value added per worker. Also, it can be declared that the share of land area equipped for irrigation in 2018 in all of land area in %, (LandEqIr186) and exchange rates (ExcRate107) in 2010 have very strong correlations.
The future trend of agricultural prosperity should follow the more dynamic development in mechanization and more advanced value added exported agricultural products instead of partly manufactured or basic products exported of this sector in cases of the selected countries. In case of China the main issue as main strategy is that China can realise such an economic growth and innovative mechanization prosperity, by result of which the mechanization and advanced agro-business development can withdrawal considerable human resources from agricultural sector to the other industrial and seravice sectors by highly educated human resources. This transfer of human resources will be realised toward industrial prosperity even industry included in agro-business for producing machines and equipment for basic agricultural production and increasing every-day life manufactured consuming products for consumers in China and considerable part of the world market by using “One Belt and One Road” Project. Somehow it can be declared that the Achilles corner of the world economy is China and Achilles corner of the economy of China is the innovative mechanization of agricultural sector and agro-business.
The agro-business supplies mechanization demand of the agricultural sector and implements manufacturing output of agricultural sector. The agricultural sector of China can only provide such more human resource, who can become well educated and skilled one in other economic, industrial and service sectors.
There is another example, namely history of US. The civil war between North and South in the second half of the nineteenth century, which focused on the creating unified single economic and social state, where the transfer of human resources was needed from agricultural sector mostly in South areas to industrial sectors mostly in North areas of US. In case of this American example cheap human resource flow was emphasized from agricultural sector to industrial sectors. Without these economic and social changes accompanying with human resource flow, the US could not have realised continuous industrial revolutions by the end of the nineteenth century. Also, it is a good experience for China to change the employment structure or human resource among economic sectors for the further economic prosperity. Also, for the Russia and Japan the mechanization can open the flowering prosperity for agricultural industry and its integration with other economic sectors, which can be based on the increasing the food self-sufficiency and possible competitive agricultural export and mostly in case of Japan less amount of agricultural and food import. For the future prosperity of the agricultural industry focuses on two elements, namely the production technology improvement and production concentration within farm system or family farm system in order to meet the demands of national and international even world markets.
Dunckel, M. (2013): Michigan State University Extension- November 14, 2013
Ekimova, K. – Kolmakov, V. & Polyakova, A. (2017): The credit channel of monetary policy transmission: Issues of quantitative measurement. Economic Annals-XXI, 166, 51-55. International state ecological Institute named after A.D. Sakharov.
FAOSTAT (2020): Macro indicators, Foreign Direct Investment (FDI), Land Use, Exchange rates – Annual, Government expenditure, Employment Indicators; 2010-2018, FAO, Rome
Gokhberg L. – Kuzminov I. (2017): Technological Future of the Agriculture and Food Sector in Russia. Pp. 135-141., Chapter 9 in The Global Innovation Index 2017 by Project ‘Study of Global Technology Trends: Development of Quantitative Approaches for Trend Analysis’ of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project ‘5–100’. Moscow. National Research University Higher School of Economics, Russia.
Jiang X. – Chen Y. & Wang L. (2019: Can China’s Agricultural FDI in Developing Countries Achieve a Win-Win Goal? — Enlightenment from the Literature. Sustainability 2019, 11, 41; doi:10.3390/su11010041. www.mdpi.com/journal/sustainability
IDRESC (Institutional for Digital Research & Education Statistical Consulting), (2020). Principal Components (PCA) and Exploratory Factor Analysis (EFA) with SPSS. Retrieved from: https://stats.idre.ucla.edu/spss/seminars/efa-spss/ (11 09 2020)
KNOEMA (2020): Agriculture value added per worker. World Data Atlas
Knoema: an Eldridge business, is the most comprehensive source of global decision-making data in the world. Our tools allow individuals and organizations to discover, visualize, model, and present their data and the world’s data to facilitate better decisions and better outcomes.
Lentner, Cs. – Vasa, L. – Kolozsi, P. & Zéman, Z. (2019). New dimensions of internal controls in banking after the GFC. Economic Annals, 176(3-4), 38–48. doi:10.21003/ea.V176-04.
McCormick, K. – Salcedo, J. – Peck, J. – Wheeler, A. & Verlen, J. (2017). SPSS Statistics for Data Analysis and Visualization. Published by John Wiley & Sons, Inc. Indianapolis, US
Statista (2021): Average farm size in the United States from 2000 to 2020 (in acres). Prices do not include sales tax.
Total number of farms in the United States from 2000 to 2020. Prices do not include
sales tax. https://www.statista.com/statistics/196103/number-of-farms-in-the-us-since-2000/
Sycheva, I.N. – Ovchinnikov, Y.L. – Permyakova, E.S. & Voronkova, O.Y. (2017): Organic production at long-fallow lands as a strategic resource of the food import substitution policy. Espacios, 38(33).
USDA (United States Department of Agriculture, 2007): Economic Research Service 2007. Family Farm Report.
United States Department of Agriculture (FY 2021): BUDGET SUMMARY, Washington 2021, p. 112.
Voronkova, O. Y. – Akhmetshin E. M. – Sycheva, L. N. – Shpakova, R. N. – Pashkova L. & Poltarykhin, A. L. (2018): Economic Mechanism of Regulating Land Relations in the Agricultural Sector of Russia. Pp. 280-291. European Research Studies Journal Volume XXI, Issue 4, 2018
World Bank (2020): GDP per capita. World Bank data, 2020.
Zhou, Zhi-Z. (2014): Analysis of the Structure of Foreign Direct Investment in China’s Agriculture. International Conference on Economic Management and Trade Cooperation (EMTC 2014), Published by Atlantis Press, pp. 489-493
Dr. Sándor J. Zsarnóczai
Institute of Environmental Engineering, Rejtô Sándor Faculty of Light Industry and Environmental Protecting Engineering, Óbuda University, Budapest, Hungary email@example.com
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