Descriptive statistics for all vectors.
\r\n\tAnimal food additives are products used in animal nutrition for purposes of improving the quality of feed or to improve the animal’s performance and health. Other additives can be used to enhance digestibility or even flavour of feed materials. In addition, feed additives are known which improve the quality of compound feed production; consequently e.g. they improve the quality of the granulated mixed diet.
\r\n\r\n\tGenerally feed additives could be divided into five groups:
\r\n\t1.Technological additives which influence the technological aspects of the diet to improve its handling or hygiene characteristics.
\r\n\t2. Sensory additives which improve the palatability of a diet by stimulating appetite, usually through the effect these products have on the flavour or colour.
\r\n\t3. Nutritional additives, such additives are specific nutrient(s) required by the animal for optimal production.
\r\n\t4.Zootechnical additives which improve the nutrient status of the animal, not by providing specific nutrients, but by enabling more efficient use of the nutrients present in the diet, in other words, it increases the efficiency of production.
\r\n\t5. In poultry nutrition: Coccidiostats and Histomonostats which widely used to control intestinal health of poultry through direct effects on the parasitic organism concerned.
\r\n\tThe aim of the book is to present the impact of the most important feed additives on the animal production, to demonstrate their mode of action, to show their effect on intermediate metabolism and heath status of livestock and to suggest how to use the different feed additives in animal nutrition to produce high quality and safety animal origin foodstuffs for human consumer.
",isbn:"978-1-83969-404-2",printIsbn:"978-1-83969-403-5",pdfIsbn:"978-1-83969-405-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"8ffe43a82ac48b309abc3632bbf3efd0",bookSignature:"Prof. László Babinszky",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10496.jpg",keywords:"Technological Feed Additives, Feed Industry, Quality of Compound Feed, Non-Antibiotic Growth Promoter, Product Quality, Additive Enzymes, Digestibility of Nutrients, NSP Enzymes, Farm Animals, Livestock, Immunity, Microbiome",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 24th 2020",dateEndSecondStepPublish:"December 22nd 2020",dateEndThirdStepPublish:"February 20th 2021",dateEndFourthStepPublish:"May 11th 2021",dateEndFifthStepPublish:"July 10th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Professor Emeritus from the University of Debrecen, Hungary who authored 297 publications (papers, book chapters) and edited 3 books. Member of various committees and chairman of the World Conference of Innovative Animal Nutrition and Feeding (WIANF).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.jpg",biography:"László Babinszky is Professor Emeritus of animal nutrition at the University of Debrecen, Hungary. From 1984 to 1985 he worked at the Agricultural University in Wageningen and in the Institute for Livestock Feeding and Nutrition in Lelystad (the Netherlands). He also worked at the Agricultural University of Vienna in the Institute for Animal Breeding and Nutrition (Austria) and in the Oscar Kellner Research Institute in Rostock (Germany). From 1988 to 1992, he worked in the Department of Animal Nutrition (Agricultural University in Wageningen). In 1992 he obtained a PhD degree in animal nutrition from the University of Wageningen.He has authored 297 publications (papers, book chapters). He edited 3 books and 14 international conference proceedings. His total number of citation is 407. \r\nHe is member of various committees e.g.: American Society of Animal Science (ASAS, USA); the editorial board of the Acta Agriculturae Scandinavica, Section A- Animal Science (Norway); KRMIVA, Journal of Animal Nutrition (Croatia), Austin Food Sciences (NJ, USA), E-Cronicon Nutrition (UK), SciTz Nutrition and Food Science (DE, USA), Journal of Medical Chemistry and Toxicology (NJ, USA), Current Research in Food Technology and Nutritional Sciences (USA). 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"72768",title:"China-Africa Investments and Economic Growth in Africa",doi:"10.5772/intechopen.89444",slug:"china-africa-investments-and-economic-growth-in-africa",body:'As the global economy expands, market activities grow and become more complex leading to product heterogeneity. Product heterogeneity increases the pace of competition which then leads to scarcity of resources. Partnerships become an official platform where economies cooperate mutually to boost trade and facilitate the flow of economic resources under less stringent rules. Non-partners are exempted from the rules and are made to go through all the complexities in trade and resource transfer.
Therefore, cooperations are developed to favor partners within a group. Strong economies help the weaker ones with favorable economic packages to sustain the partnership. China is a key player in partnership agreements. It has several economic engagements with both developed and less developed economies. It connects with Africa through what is known as the Forum on China-Africa Cooperation (FOCAC). FOCAC was established in 2000. Since it developments, several economic packages and investment programs have been developed to boost trade and infrastructural development in the region.
FOCAC was not the first economic cooperation China has had with Africa. The long-standing friendship between China and Africa can be traced 600 years ago via the legendary expeditions of Chinese navigator Zheng He, whose fleet reached the East African shores four times. In the 1960s, China developed the TAZARA Railway line between Tanzania and Zambia, which served as a monument of what the two regions can achieve together. The cooperation did not progress further until the mid-1900s [1]. FOCAC is the most successful cooperation China has ever had with Africa.
The FOCAC has supported many growth initiatives in Africa, the latest project being “China One Belt Road or Maritime silk road initiatives,” connecting Africa with railway and shipping link to major markets in the Middle East and Central Asia. Despite other interests, it is believed that China is much focused on developing infrastructural systems in Africa through the provision of loans and financial investment, hence a preferred partner for most African economies. Currently, China’s investments are found in at least 46 countries in the region under different investment portfolios [2]. At least 2200 Chinese enterprises, both private and state-owned, are operating in Africa [3]. China’s social program launched in 2013 has developed hundreds of educational projects, medical institutions, anti-malarial centers, and agricultural technologies [4]. China’s banks, notably the People’s Bank of China, the China Development Bank, and the Export-Import Bank of China (Exim Bank of China), have financed large-scale investment projects in Africa.
In addition to financing projects in Africa, China is also interested in sustaining its industrial program in the mainland. It has a strong manufacturing sector and a wider market share (both domestic and international), which requires constant resource and commodity supplies to sustain activities and supply its partners. With such a wider market share, China is scared of losing its activities because of the resource or commodity gap. It cannot have a sustainable operation without partnering with other resource-rich economies, hence cooperating with Africa. On the one hand, China’s domestic resource and commodity capacities are currently under pressure because of higher demands. Africa, on the other hand, has vast resource and commodity potentials yet in a less industrialized zone. It has an emerging industrial sector facing infrastructural, technical, and funding challenges. The economy of China, however, is stronger in that sector, hence a good reason for a mutual partnership. China’s technical competencies and industrial experiences can help Africa to build an effective industrial economy, while African resources and commodity potential can help sustain China’s manufacturing sectors.
At this moment, China is well positioned to grow favorably. It has a large domestic and international market (due to growing population), efficient human capital, and a low-wage economy, which gives it a competitive edge over other industrialized economies. As a result of this, China attracts more industries and companies into its space. This reallocation has further raised the inflow of FDI into the economy and widened its position as a global leader in manufacturing activities, overtaking the US. It is now in high demand for resource and commodity supplies to feed anthropogenic activities. Through economic cooperation, China is partnering with resource-rich economies to boost supplies and to close the resource gap in the domestic economy [5].
China imports a significant part of its economic resources and commodity from Africa. It also has private companies operating in the agricultural and mining sectors. The resources and commodities produced are transferred to China to feed industries. Private businesses in Africa import processed and technical goods from China to feed and support domestic activities. Financial investment via loans supports infrastructural projects in Africa, hence reducing the financial burdens on the budget. China is Africa’s major trading partner; it buys one-quarter of Africa’s trade. China’s energy demand has doubled in the last decades [6]. It buys at least one-third of African oil supplies, particularly from Egypt, Libya, and Tunisia, and Nigeria.
It also imports non-oil products from various parts of Africa (coal is imported from South Africa, ore from Gabon, timber from Equatorial Guinea, and copper from Zambia). Some local exporters export primary resources to China and in return import consumables such as textiles, pharmaceuticals, technological systems, and telecommunication to boost the small- and medium-scale sectors [7]. FOCAC is the key facilitator in all these activities. In 2012, Africa’s total export ($3.1 billion) to China was one-third of China’s total export to Africa ($9.4 billion). In 2011, it was four times the size of China’s total export to Africa [8].
China imports more from Africa than what Africa imports from China. According to Figure 1, from 1995 to 2012, China’s import by Africans was three times the size of China’s export by Africans on average. In the same period, China’s FDI inflow to Africa grew by 800-fold. It was significantly high in 2008, where it grew by 2000-fold but dropped significantly in 500-fold in 2009. This was induced by the cascading effect of the global financial bubble. It later picked up massively in 2011 to 1200-fold. On aggregate, China has invested about $40 billion of FDI stocks in major African economies. About $13.8 billion of the stock is capital injection, which went into infrastructural development. The inflows of African FDI to China from 1995 to 2000 grew about two-folds [9].
Shows the pattern of China’s export by Africans, China’s import by Africans, China’s FDI inflows to Africa, and unemployment. Sources: China Statistical Year Book 2012, and World Development Indicator (WDI).
Foreign direct investment (FDI) is a key indicator of economic development. It stimulates the performance of the factor of production to boost economic growth [10]. It is a reliable source of technical resources and financial capital [11]. As a result of these, policies and programs are formulated to facilitate its movement across sectors [12]. FDI also promotes efficient output performance of the human capital [13]. It is considered the cheapest source of technical and financial capital to support growth [14]. In a broader spectrum, FDI’s negotiations deepen group commitment, as investors prefer working with a group rather than a single economy [14].
Developing economies receive FDIs from various regions but they gain less from it because of institutional and infrastructural challenges. An economy with an established institutions and proper infrastructural systems is able to coordinate effectively with the flow of FDI [15, 16, 17]. In such economies, FDIs are properly allocated without compromising activities of local investors. FDIs are directed into areas of the economy where local investors have limited capacities to operate, thus widening the economic scope [18, 19]. Investors’ confidence is high in such economies because of low operational cost and high investment returns.
A dysfunctional institution creates unhealthy competition between foreign and local investors [20]. Unhealthy competition freezes the activities of local investors causing them to exit the market (because of capital and skills disadvantage), creating a foreign-dominated market. An effective institution properly coordinates FDI inflows across all sectors. According to the World Investment Report (WIR) (2012), about 60% of FDI inflows in Africa go to capital intensive activities such as mining and oil and gas activities [21]. Capital intensive requires high capitalization which the local investors have no capacity to operate.
A review of growth literature highlights some studies on FDI. They examined the key determinants of FDI at the national level. Similar to what we highlighted in the foregone paragraph, institutions and infrastructure are the main prerequisite for affective FDI programs. For instance, Adewuni [1] examined Nigeria-China economic cooperation. The findings revealed a less than expected growth between FDI and economic growth, citing institutions and infrastructural and human capital as the main challenges. Kamara [21] in broader studies examined several Sub-Sahara African (SSA) economies. Busse and Groizard [15] also examined a national economy. Despite finding a positive growth relationship between FDI and economic growth, the finding also cited low human capital and weak infrastructural systems as the main drawbacks. However, AbuAl-Foul [22] found mixed outcomes in a di-country study between Morocco and Tunisia. The economy of Morocco experienced a resilient growth link between FDI and growth while the economy of Tunisia experienced otherwise. All the studies gave insights in understanding FDI-growth relation, particularly at the national level [22]. However, there remains a gap at the regional level that needs to be filled.
This chapter is examining Africa regional economy, looking at the impact of key macroeconomic indicators particularly China’s FDI on regional economic growth using at least two decades of data. The remaining macroeconomic indicators include export, import, unemployment, and trade openness. Furthermore, the chapter is examining the impact of World and US FDI inflow on African economic growth using Granger causality test and autoregressive distributed lag (ARDL) model. The ARDL model will help test the short- and long-run effects of FDI on economic growth. Granger causality technique will help examine the causal relationship between economic growth and all the macroeconomic indicators. Finally, the chapter will look at whether Okun’s law exists between unemployment and economic growth.
The outcome of this chapter has a two-fold effect; (1) inform policy regulators about the actual empirical behavior of China’s FDI on economic growth in Sub-Sahara Africa, (2) Policy regulators will be able to make effective allocation of FDI resources to areas of greater impact in the economy. The recommendation session will offer some practical guidelines or policies that will boost the benefits of FDIs in creating jobs and reducing inequalities.
The rest of the chapter is organized as follows. Section two explains the methods (i.e. Autoregressive Distributed Lag (ARDL) and Granger Causality) Section three explains materials and methods for the analysis. Section four explains results and analysis. Section five offers conclusion and policy recommendations.
The vector auto-regression (VAR) of order p, denoted VAR (p), is expressed as the following [23]:
where
The vector error correction model (VECM) is expressed as follows:
where
The diagonal elements of Eq. (4) are unrestricted, so the selected series can be either I(0) or I(1). If, then Y is I(1). In contrast, if, then Y is I(0).
Eq. 2 is expanded to include all the regressors for the study, as shown below for later bound testing after estimation.
where
Eq. (4) also can be viewed as an ARDL of order (p, q, r). Eq. (4) indicates that economic growth tends to be influenced and explained by its past values. The structural lags are established by using minimum Akaike’s information criteria (AIC). After regression of Eq. (4), the Wald test (F-statistic) is used to test the long-run coefficient to check whether it is significant or not. According to Pesaran et al. [23], the null and alternative hypotheses can be read as follows:
The computed F-statistic value will be evaluated with the critical values tabulated in Table CI (iii) of Pesaran et al. [23] paper. As explained in Table CI (iii), the lower bound critical values assume the explanatory variables are integrated of order 0, or I(0), while the upper bound critical values assume the explanatory variables are integrated of order one, or I(1). Therefore, if the computed F-statistic is smaller than the lower bound value, then the null hypothesis is not rejected, which implies that there is no long-run relationship between economic growth and its determinants. However, if the computed F-statistic is greater than the upper bound value, then there is a long-run relationship between economic growth and its determinants. But, if the computed F-statistic falls between the lower and upper bound values, then the results are inconclusive.
Granger causality analysis is an analytical tool for examining whether a one-time series can correctly predict the other
where Yt and Xt represent the two time series at t.
Peculiar to this study, mathematically, we introduce the lag of each series such as China’s FDI inflows to Africa (CFDIITA), China’s export to Africa (CEBA), China’s import to Africa (CIBA), US FDI inflows to Africa (USFDIITA), openness (OPEN), and secondary enrolment (SSE) into equations for better prediction. Our model is thus expressed as follows:
The research considered point annual FDI data but not accumulated stock data. Two models were used for the estimation, GMM and Granger causality method.
As a policy-based paper, the purpose of this chapter is to find an empirical justification for what has become a popular dialog in the economic environment, “China sudden interest in Africa.” Has China’s increasing presence in Africa via bilateral trade and investment link during the last two decades impacted significantly on Africa’s macroeconomic indicators such as GDP per capita, unemployment, and human capital development. Considering Okun’s law, there a link between economic growth and unemployment in the region? These questions arise because of the growing domination of China’s investment in Africa vice verse that of the United States. Is the supposedly China’s economic motive plan more effective and receptive to African economies than the US in addition to political motive? The latter will be addressed in detail in the next chapter.
However, this Chapter is looking at the impact of FDI on key macroeconomic indicators in Africa using over decades of time series data from 1990 to 2014. The series include China’s FDI inflow to Africa, China’s Export to Africa, China’s import from Africa, Secondary School Enrollment (SSE) (a measure of human capital), openness index, US FDI Inflows to Africa (USFDITA), World FDI inflows to Africa (WFDIITA) and African Investment Outflows to the World (AIOTTW). Annual FDI series (rather than FDI stock1) is used in the analysis. Real GDP2, served as the dependable variable, a measure of economic growth.3 Trade openness stimulates economic growth.
Table 1 shows the log description of the vector series. The average mean of real GDP is 0.037, less than the average mean of all regressors except China’s export to Africa (CEBA) and openness (OPEN), which estimated average mean values of 0.034 and 0.010, respectively. The standard variance of all vectors is relatively a higher mean, suggesting high variation within vector indicators. Except for CIBA and UNEM, other vectors are not normally distributed (look at the Jarque-Bera test, they are not significant at 5%). In Table 2, there is a lower correlation between variables, suggesting a lower chance of perfect multicollinearity (Figure 2).
DAFDIOTW | DCFDIITA | DCEBA | DCIBA | DFDIITA | DGDP | DOPENN | DSSE | UNEM | USFDIITA | |
---|---|---|---|---|---|---|---|---|---|---|
Mean | 0.202 | 0.322 | 0.034 | 0.216 | 0.122 | 0.037 | 0.010 | 0.049 | 17.109 | 21.887 |
Median | 0.345 | 0.295 | 0.009 | 0.252 | 0.075 | 0.037 | 0.013 | 0.049 | 17.225 | 22.069 |
Max. | 1.352 | 2.220 | 0.303 | 2.228 | 0.727 | 0.075 | 0.082 | 0.095 | 17.457 | 22.995 |
Min. | −1.423 | −1.338 | −0.248 | −0.737 | −0.303 | −0.003 | −0.108 | 0.010 | 15.183 | 19.918 |
Std. dev. | 0.730 | 0.810 | 0.131 | 0.587 | 0.251 | 0.019 | 0.042 | 0.018 | 0.429 | 0.929 |
Skew. | −0.264 | 0.041 | 0.273 | 1.552 | 0.691 | −0.227 | −0.807 | 0.223 | −3.778 | −0.615 |
Kurt. | 2.423 | 3.699 | 3.100 | 7.584 | 3.176 | 3.109 | 3.987 | 3.465 | 17.661 | 2.433 |
J.B. | 0.560 | 0.434 | 0.282 | 28.096 | 1.939 | 0.218 | 3.578 | 0.416 | 283.367 | 1.605 |
Prob. | 0.756 | 0.805 | 0.868 | 0.000 | 0.379 | 0.897 | 0.167 | 0.812 | 0.000 | 0.448 |
Obs. | 22 | 21 | 22 | 22 | 24 | 25 | 25 | 24 | 25 | 21 |
Descriptive statistics for all vectors.
Log of variables from 1990 to 2014.
Source: author’s computation.
DOPEN | DSS | DFDIITA | DCIDA | DCFDIITA | DAFDIOTTW | USFDIITA | UNEM | |
---|---|---|---|---|---|---|---|---|
DOPEN | 1 | |||||||
DSS | 0.133681 | 1 | ||||||
DFDIITA | 0.117451 | −0.2984 | 1 | |||||
DCIDA | 0.6689 | 0.4875 | −0.1330 | 1 | ||||
DCFDIITA | 0.4569 | −0.0583 | 0.0472 | 0.0762 | 1 | |||
DAFDIOTTW | 0.4183 | 0.20233 | 0.113561 | 0.304829 | 0.201383 | 1 | ||
USFDIITA | 0.3837 | −0.0339 | −0.15267 | 0.465755 | 0.417959 | 0.03539 | 1 | |
UNEM | −0.2191 | 0.2461 | −0.1824 | −0.0444 | 0.0174 | 0.0237 | 0.4628 | 1 |
Correlation matrix for each series from 1990 to 2014.
The correlation matrix for all vector series from 1990 to 2014.
Source: author’s own computation.
Shows the unit root testing results for each of the series using the augmented Dickey-Fuller (ADF) technique. The series were stationary at different levels (either at I(0) or I(I)). The gaps represent missing data.
Figure 1 shows the log stationary for each series for the Africa economy forms the period 1990 to 2010 employing the augmented Dickey-Fuller unit root test. We found a stationary path for all vector series at first difference I(1) except for UNEM and USFDIITA. The break within AFDIOTW, USFDIITA, and CFDIITA stationary paths was due to missing data series. CFDIITA, CIBA, USFDIITA, and CEBA also lost some data at the beginning.
In this session, the analysis was based on the Pesaran [23] autoregressive distributed lag (ARDL) technique. There are two phases in the model: the long run and the short run [23]. Using real GDP (proxy by economic growth) as a dependent variable, the finding for both phases is presented in Table 3.
Given the principle of ARDL model, each series must be either be stationary at first different I(1) or at the level I(0). According to Dickey-Fuller unit root test in Figure 1, all the series satisfied the ARDL condition, i.e., I(0) or I(1). For instance, unemployment (UNEM) and US FDI inflow to Africa (USFDIITA) were stationary at the level I(0), while RGDP, China’s export to Africa (CEBA), China’s FDI to Africa (CFDIITA), openness (OPENN), secondary school enrollment (SSE), China’s import from Africa (CIBA), Africa FDI outflow to the World (AFDIOTTW), and FDI inflow to Africa (FDIITA) were stationary at I(1). Furthermore, all the models satisfied the conditions for multicollinearity, i.e., there is no serial correlation.
In Pesaran (2001), there are predefined critical values for making a statistical decision for the ARDL short- and long-run parameters [25]. There are different critical values for different significant levels depending on the structure of the model. Essential to this analysis is the critical values highlighted in Pesaran (2001), Table CII (iii) of page 303. It has an unrestricted intercept and no trend condition. At a 5% critical value, using ten (10) parameters point, Pesaran (2001) predefined the critical values in Table CII (iii). According to Table 3, critical values of 2.86 and 5.03 are decision results for an absolute lower I(0) and upper bound value I(1).
In Table 3, the dynamic model presented a computed F-value of 2.9595 for the short run. Based on the decision rule, it falls on the indecisive region, suggesting that China-Africa economic cooperation over the decades (in the short run) has not shown a significant effect on economic growth, i.e., indeterminate. Therefore, a lot is expected to be done for Africa’s economic growth to experience the significance of China’s FDI to Africa, China’s export and import to Africa, US FDI to Africa, and Africa openness policies in the short run.
However, some selective series reported a decisive outcome. For example, F-computed value (22.47) for China’s FDI inflows to Africa (CFDIITA) and FDI to Africa (FDIITA) critical value fell above the upper bound limit (5.03), suggesting a significant effect on regional economic growth. This is consistent with the theory that states that FDI stimulates growth [22]. This analysis has shown the significance of China’s FDI on African growth.
In Table 3, in the long run, the F-computed value (18.94) fell above the upper limit of Pesaran [23] critical value (5.03), (i.e., it is in the acceptance region). The result suggests that overall change in all the series has a significant effect on economic growth. The estimated F-value (14.677) of unemployment, secondary school enrollment, and China’s FDI to Africa is above the upper limit of the critical value, hence has a significant effect on economic growth. On this account, a change in China’s FDI to Africa (CFDIITA) in the long-run will generally boost regional economic growth.
An individual analysis of each series or indicator reported different outcomes. In Table 3, all the series except China’s FDI Inflows to Africa (CFDIITA) and Unemployment (UNEMP) in the long-run experienced an insignificant relationship with regional economic growth (at 5%). The impact of UNEMP on economic growth was positive at 5%. Highlighting the significance of education in economic development. A change in the quality of human capital will further boost regional economic growth.
Concerning China’s FDI to Africa (CFDITA) series, in the long-run, a change leads to a fall in economic growth. A similar outcome was estimated in the short-run. These outcomes give policy regulators a fair notion about the behavior of Chinese investments in Africa. If the evidence is considered enough, African economies must question the credibility of the investment. A contrary outcome was assumed before the analysis because of Chinese investment domination vice versa other developed economies. On this caveat, policy regulators in the region must review Chinese economic potentials and effectively attract investments from areas they have a comparative advantage or greater efficiencies that will benefit African economies. When successfully achieved, African economies will gain favorably from Chinese growing investments in the region. In the immediate term, governments across Africa can push most of China\'s FDI into developing labor-intensive programs or value addition industrial activities.
In model 1, a change in unemployment leads to a decline in economic growth in the long run. On the contrary, economic growth widens the unemployment gap in the region. This shows the lack of consistency between the flow of FDI and how it is applied in reducing unemployment. Mis allocation of investment harms development and widens the gap in inequalities and unemployment. Unemployment is a leakage in development and can affect economic growth negatively if loosely handled. Currently, at least 20% of the regional population remains unemployed. This paints a glooming picture for the region\'s future development if practical steps are not proposed today to boost job creation.
In Table 4, the impact of World FDI inflow in Africa (FDIIITA) on economic growth was positive. African FDI outflow to the World (AFDIOTTW) was found to have a positive effect on economic growth. On the contrary, the US FDI Inflows to Africa (USFDIITA), China’s Export to Africa (CEBA), and China’s Import to Africa (CIBA) reported an insignificant effect on economic growth. The effect of openness (OPENN) was also insignificant. The latter does not imply African economies are closed or are not fully integrated into the global economy but rather suggests that less is gained from external participation. The majority of the exporting activities are primary-based. Primary-based activities have lower economic returns compared to capital or processed activities. In the last decade, the net wealth of African global trade inclusiveness was less than 10%. This figure is small compared to the combined volume of China and the United States, about 18% of world trade. Meanwhile, Africa is a significant player in the commodity and resource activities (led by Equatorial Guinea, Nigeria, Liberia, Kenya, Botswana, and Tunisia; mining: Ghana, South Africa, and others).
The US FDI inflow to Africa was found to be insignificant on African economic growth. This is due to the US losing interest in the African economy. The US FDI stock is by far the highest in African compared to China. But lately, it is declining while that of China is rising because of increasing China’s interest in Africa. It is argued that the model of operation in Africa by China businesses is very much liked by the national governments because it is mainly economic. The US model, on the other hand, has a strong element of political interference which is not very much liked by the national governments. But research shows that a stable environment promotes an effective growth [24].
Granger causality test is used for forecasting between two series in an analysis. When two series are co-integrated then, there is likelihood of causality in at least one of the directions [25]. For instance, FDI and GDP are co-related, which implies that a change in one can cause the other to change, vice versa. To observe empirically the causality test for all the series in this study, Granger causality technique was employed. The results are presented in Table 5. Conclusively, there are situations of a unidirectional effect for some of the series.
Variables | Short-run coefficients | Variables | Long-run coefficients |
---|---|---|---|
Constant term | −1100 (−6.080) | ||
D(RGDP(−1)) | 0.989 (−0.232) | D(RGD) | −0.792 (−0.193) |
D(CEBA(−1)) | −8.513 (−3.502) | D(CEBA) | 49.783 (−13.383) |
D(CFDITA(−1)) | −4.518 (0.712)* | D(CFDITA) | −8.865 (1.579)* |
D(OPEN(−1)) | −8.908 (−8.597) | D(OPEN) | −8.975 (−8.500) |
D(UNEMP(−1)) | 59.566 (−12.056) | D(UNEMP) | 942.98 (182.909)* |
D(USFDIITA(−1)) | 0.658 (−0.518) | D(USFDIITA) | 0.026 (−0.770) |
D(SSE(−1)) | −1026.498 (−420.350) | D(SSE) | 170.536 (363.717)* |
D(FDIITA(−1)) | 1.783 (0.3554)* | D(FDIITA) | −0.0124 (−0.660) |
D(CIBA(−1)) | 0.687 (−2.641) | D(CIBA) | −13.413 (−3.304) |
D(AFDIOTTW(−1)) | −0.654 (−1.272) | D(AFDIOTTW) | 5.798 (−2.563) |
R-squared | 0.896 | R-squared | OBS: 23 |
F-computed | 2.959 | F-computed:18.94 |
Dynamic ARDL Model Result.
Real GDP as dependent variable (Model 1).
Absolute lower I(0) and upper bound value I(1) of 2.86 and 5.03. The standard errors are reported in brackets. Significance levels: *** if p < 1%, ** if p < 5%. and * if p < 10% [22].
Source: author’s computation.
Static model 2 | |
---|---|
Variables | OLS regression |
Constant term | 22..900 |
(350.941)** | |
D(CEBA) | −14.446 |
(−8.226) | |
D(CFDITA) | −0.262 |
(−0.966) | |
D(OPEN) | −9.240 |
(7.734) | |
D(UNEMP) | −397.358 |
−815.034 | |
D(USFDIITA) | 0.884 |
−0.948 | |
D(SSE) | 175.769 |
(99.78)** | |
D(FDIITA) | 2.217 |
(0.523)** | |
D(CIBA) | 2.453 |
−2.591 | |
D(AFDIOTTW) | 3.567 |
(1.438)* | |
R-SQUARED: | 23 |
Static Model-Real GDP as Dependent Variable.
The standard errors reported in brackets. Significance levels: *** if p < 1%, ** if p < 5%, and * if p < 10%.
Source: author’s computation.
Variable | F-stats | Prob. | |
---|---|---|---|
OPEN does not Granger cause RGDP | 24 | 8.19958 | 0.0093 |
RGDP does not Granger cause OPENN | 0.01898 | 0.8917 | |
CIBA does not Granger cause RGDP | 24 | 0.44314 | 0.5129 |
RGDP does not Granger cause CIBA | 15.4376 | 0.0008 | |
AFDIOTTW does not Granger cause RGDP | 24 | 2.47903 | 0.1303 |
RGDP does not Granger cause AFDIOTTW | 8.51158 | 0.0082 | |
CEBA does not Granger cause RGDP | 24 | 0.12593 | 0.7262 |
RGDP does not Granger cause CEBA | 7.86194 | 0.0106 | |
CFDIITA does not Granger cause RGDP | 24 | 0.90823 | 0.3514 |
RGDP does not Granger cause CFDIITA | 5.13697 | 0.0341 | |
UNEM does not Granger cause RGDP | 24 | 3.47945 | 0.0762 |
RGDP does not Granger cause UNEM | 51.5813 | 0.074 | |
UNFDIITA does not Granger cause H | 24 | 0.24272 | 0.6274 |
H does not Granger cause UNFDIITA | 5.25532 | 0.0323 | |
SSE does not Granger cause RGDP | 24 | 2.04574 | 0.1673 |
RGDP does not Granger cause SSE | 0.44133 | 0.5137 | |
FDIITA does not Granger cause RGDP | 24 | 1.21995 | 0.2819 |
RGDP does not Granger cause FDIITA | 5.95881 | 0.0236 |
Granger causality test results.
Source: author’s computation.
Table 5 summarizes the results for each series. According to Table 5, there is a uni-directional causality link between China’s Export by Africa (CEBA), openness (OPEN), Africa’s FDI Outflow Around the World (AFDIOTW), China’s Import from Africa (CIBA), and China’s FDI inflow to Africa (CFDIITA) on economic growth (GDP). This suggests that a change in any of the determinants or indicators will influence regional economic growth (but the reserve is not certain). As a result of this, proper allocation of FDI inflow from various sources including those from China and the US will directly boost regional economic growth. This certainly shows that Chinese investment in Africa is adding more to development. The ARDL findings in Table 3 confirm this situation.
On the contrary, there was no causality link between unemployment and economic growth. There was also no granger causality link between secondary enrollment (i.e., human capital) and economic growth. In theory, unemployment is a leakage in development when found to directly influence economic growth. Unemployment imposes a heavy burden on development via social intervention programs. As a result of that, minimizing the unemployment gap is a major aim for every economy. An increase in secondary school enrollment closes the literacy gap and increases the quality of human capital.
The FOCAC cooperation has benefited China’s economies more than it did for African because of growth hindering factors. In the form of foreign direct and portfolio investment, Chinese activities have grown in the region and are seen everywhere. China knows exactly what it wants from Africa while Africa is yet to wakeup. Africa is still assuming it will gain from China engagement.
Is African economic performance growing as a result of China\'s economic cooperation or is yet to happen? This Chapter examined this question from the preview of FDI and growth analysis using at least two decades of FDI data. The chapter also examined the effect of US and the World FDI on growth using Autoregressive Distributive Lags (ARDL) and Granger Causality models. According to the ARDL model, there was a positive growth relationship between China\'s FDI and African economic growth in the long term but not the short term. It was positive for the World FDI inflow to African. However, the effect of US FDI inflows to Africa was insignificant.
Change in human capital positively influences regional economic growth. There was no evidence of Okums Law as economic growth increases with unemployment, suggesting a lack of growth in the job market. Activities prevailing activities in the government and non-government sectors are not enough to bridge the gap in unemployment. The impact of openness i.e. economic inclusiveness was unexpectedly negative with economic growth in all models. This does not suggest, the region is a closed economy.
The African community will gain significantly from China\'s investment engagements if the following recommendations are factored in the region growth plans.
In the African economy, resolving growth issues are necessary, if gaining the most from FDI is the ultimate objective. Lingering growth problems will continue to hinder effective investment allocations. Without specifically outlining the core issues (facing development in the region) and actually resolving them is a recipe for underdevelopment. For example, Oil-producing nations need to go beyond crude oil activities which has a lower market price to processing activities which has a higher market price. Continuing with temporarily fixed and front-loaded deals with China will not resolve the region\'s major problems. China in particular knows that it wants from Africa and as a result deals with African in that regard. In the same vein, African economies need to know what they want to influence investment programs with China. They will be able to attract investments that will resolve their growth issues other than going for anything at all which has a long-run effect of collapsing the domestic activities and the exporting sectors. Diversification programs will have a greater impact as a result while the non-oil sectors will be well developed.
The African community can leverage China\'s economic interest to attract investment resources to bridge the infrastructural gaps facing development in the region. As the majority of countries experiencing heavy financial depts from doner organizations (the IMF and World Bank and) thereby losing their creditworthiness. The most viable approach to continue expanding infrastructural development in the region is to adopt and implement the Mutual or Pooled Growth Model (MGM/PGM) investment plan with China. This strategy as the name implies requires China and African economies to initiate a “susu” plan together. Depending on the agreement, both countries (i.e. China and any other country in African or all ) can pull a fixed amount of funds together to be invested in a viable structural program or project in a partner country (say an African country) for at least two years and later pull another for the remaining country (say China) for a similar or different project/s. Both countries after the two years will account for the funds to avoid miss-appropriateness. This type of financing or investment is unique because unlike the IMF funding system, it has no interest commitment hence reducing the debt to GDP ratio for partner economies.
Africa\'s manufacturing sector remains underdeveloped, yet China is an industrial hub. Africa is an endowed region with diverse resource potential, hence a suitable place to site processing and manufacturing industries. The lack of effective technical competencies is delaying industrial programs in the region. China-Africa cooperation is a forum that can help Africa close the technical gap and boost industrial development. The cooperation can be used to transfer technical resources to support the sector. Through government policies, young entrepreneurial visions can be supported by given special incentives such as tax holidays for at least one year, technical and skills training, and others to sustain activities. The government through its machinery can also protect young vision from competitions. This will allow them to grow into huge exporting industries and create more jobs to reduce unemployment.
Lastly, Africa needs to reform its investment policies with China. Observing China\'s past partnership engagements with the ASEAN community and other rising economies will help gain wisdom and help shape future engagement with China. In the future, Africa community will be able to develop proper economic deals with China via trade and investment. Again, the US has had long-standing trade and investment history with China. Most of such engagements have had a couple of successes and as well as failures to learn from.
Vegetable seed proteins are widely used as ingredients in the food industry. Peas (Pisum sativum L.) have grown to be an essential vegetable source of proteins in addition to a likely replacement for soybean [1]. The better acceptance of pea protein-rich food is due to pea manifold attributes, excellent functional qualities in meals programs, high vitamin value, accessibility, and comparatively small cost. Dry peas have 20–30% lysine content. Pea proteins are mainly storage protein composed of albumins and two globulins, legumin and vicilin [2]. Besides, these protein-rich foods are characterized by higher lysine content. The primary pea storage proteins referred to as legumin and pea legumin is hexamer owning a molecular sector (Mw) ∼ 320 to 380 kDa. The genuinely bioavailable protein has a pile of easily digested protein, getting a gentle flavour. Unlike extra protein powders with among the top eight allergens as soy, dairy-derived whey, pea protein-rich foods are hypoallergenic; thus it’s a great protein alternative for each one of those with and with absolutely no allergies [3]. Pea protein dietary supplements are made in many items. The flexible protein has a packaging that is in unflavored and flavoured blends. Additionally, the pea seeds are loaded with fibre, vitamins, along with micro and macroelements [1].
\nProteins obtained from plant sources are expanding ingredient of the marketplace in part due to consumer preferences and their comparatively small cost in contrast to animal-derived proteins [4]. Pea ingredients additionally are attractive to the food market because of their low allergenicity, nutritional value and non-GMO status. While pea does consist of antinutritional components which can inhibit digestion and may have various prospective deleterious effects pea is still viewed as a too wholesome meal as well as is linked with total health benefits beyond elementary nutrition. The health benefits of pea seed proteins derive primarily from the qualities of starch, vitamins, fibre, protein, phytochemicals and minerals in peas. In this direction, mineral contents and the vitamin of peas may play crucial roles in the protection against deficiency-related diseases, particularly those regarding deficiencies of Folate or Selenium. Peas include a range of phytochemicals previously considered just as antinutritive factors. These contain polyphenolics, in coloured seed layer sorts particularly, that contains anticarcinogenic and antioxidant activity, saponins which might exhibit anticarcinogenic and hypocholesterolemic activity, as well galactose oligosaccharides which might exert beneficial prebiotic consequences within the large intestine [5, 6]. Many strategies for the extraction of protein from pea flours have been reported. Each extraction method might select for different protein sorts which consequently influences the final composition and functionality of the isolated product. In this chapter, we have compiled the information related to pea proteins targeting isolation methods, extraction, and of the seed proteins in pea.
\nProtein content in pea lies in a range of 21 to 30 per cent with an average of 23 per cent depending on genotype, growing environment and related factors [6]. The overall phenotypic expression of protein content is a result of environmental as well as genotypic components. The cultivars originating from various geographical areas show a range of protein content levels (Table 1). The heritability estimates show that pea protein content and quality is a heritable trait [7, 8], thus target for improvement through selection in breeding programs. Changes in environmental factors such as temperature, rainfall, soil type result in a differential response in performance of pea cultivars; thus multi-location and multi-year data is required for final estimation of protein content [9, 10, 11]. Most of the nitrogen supplies during fruit development relies on assimilation after the flowering and only a portion of the collection of nitrogen depends on assimilation before flower development [12]. It has been reported that low rainfall and high temperature is positively correlated with high protein content in pea genotypes [10, 13]. A total of 7% high protein content was observed in pea crop raised in dry location than another location having 209 mm higher rainfall indicating role of low rainfall has a significant influence on protein content [10]. However, in another study, there was 1.5% rise in pea protein content between the crop raised in the periodic wilting moisture content of 10 percent versus 26 per cent moisture content at field capacity [14]. In addition, seed yield is known to be negatively correlated with protein content, and these conclusions were made by various independent studies in different years and locations [11, 13, 14]. The dry matter in seed constitutes approximately 50% starch [15, 16]. The dietary fibre and total protein content account for 20 and 24% of the dry matter, respectively. Whereas, 2.5% of dry matter is contributed by lipids [17]. Protein content and starch are highly variable, but other components show little variation [15]. It was found in a study that protein content was negatively correlated with lipid, starch, ash, fibre content and soluble sugar and among these variations in starch content had a significant effect on protein content levels [18]. This study was conducted at four locations in Canada using dehulled pea cultivar, and it was observed that protein content of the cultivar was variable across locations showing levels 14.5%, 18.3%, 24.3%, and 28.5%. The starch synthesis was reported to be a critical factor in determining pea protein content as smooth seeded pea having a higher content of amylopectin and starch showing lower protein levels (23–31%) than wrinkled pea seeds (26–33%) [19]. Recessive gene account for higher protein levels in wrinkled pea seeds.
\nPea seeds | \nProtein content | \nCountry | \nReference | \n
---|---|---|---|
\nPisum sativum L. cv. Ucero\n | \n25.48 | \nSpain | \n[20] | \n
\nPisum sativum L. cv. Ramrod\n | \n21.17 | \nSpain | \n[20] | \n
\nPisum sativum L. cv. Agra\n | \n22.90 | \nSpain | \n[20] | \n
\nPisum sativum L. cv. Maja\n | \n24.21 | \nSerbia | \n[21] | \n
\nPisum sativum L. cv. Calvedon\n | \n27.70 | \nSerbia | \n[21] | \n
\nPisum sativum L. cv. Miracle of America\n | \n22.31 | \nSerbia | \n[21] | \n
\nPisum sativum L. cv. Sprinter\n | \n23.98 | \nTurkey | \n[6] | \n
\nPisum sativum L. cv. Manuell\n | \n23.26 | \nTurkey | \n[6] | \n
\nPisum sativum L. cv. Century\n | \n23.9 | \nCanada | \n[22] | \n
\nPisum sativum L. cv. Trapper\n | \n24.5 | \nCanada | \n[22] | \n
\nPisum sativum L. cv. Delviche Scotch Green\n | \n24.0 | \nCanada | \n[22] | \n
\nPisum sativum L. cv. Ceser\n | \n24.9 | \nCanada | \n[22] | \n
\nPisum sativum L. cv. CD647 4\n | \n24.9 | \nHungry | \n[22] | \n
Protein content of famous pea cultivars grown in various parts of the world.
Peas are an excellent source of human nutrition owing to 25% protein in seeds [1], and it has a comparable amino acid (AA) profile to other legumes. Pea protein contains a lesser amount of sulphur amino acids, i.e., methionine and cystine and lower levels of tryptophan AA, whereas high levels of lysine AA [23]. The bioactive peptides of pulses are popularized due to affordable prices when compared with animal protein [24]. During the processing of food, microbial agents or digestive enzymes cause the hydrolysis of large proteins and release bioactive peptides which are usually 3–20 AA long [25]. Nutritional and functional properties food protein are studied using bioactive peptides obtained by hydrolysis through enzymatic action [26]. AA composition of a peptide is the key to its biological activity [24]. Oxidative stress damage in human beings can be prevented by developing nutraceuticals and foods using such peptides. High levels of antioxidants in natural foods can be even more appealing than synthetic counterparts [24, 27]. In a study by Amarakoon [28] the amino acid profile of pea showed that pea grown in central Europe was rich in leucine, lysine and arginine which were sufficient for a normal diet. The amino acid profiles of pea were compared with soybean and reference FAO/WHO requirements. The essential AA content was higher in pea in comparison to soybean. The lysine content was 6.39–6.93/16gN in pea, which was also higher than soybean. Another comparison of AA profile of flour and isolates and concentrates of protein of pea, soybean and lupin was made by Tomoskozi et al. [29]. They concluded that composition of AA was the same in all compounds with the highest amount of glutamine and comparatively lower amounts of aspartic acid, lysine and arginine and smallest contributions of methionine, cysteine and tryptophan.
\nIn comparison to soybean and lupin, pea compounds had high levels of arginine, methionine and valine and comparatively low levels of cysteine and glumatic acid. The muscle development and growth in human body is dependent on postprandial essential amino acid availability particularly leucine [30]. AA composition, essential AA content and anti-nutritional factors regulate the availability of essential AA [31]. Thus, variation in AA composition particularly in essential AA are desirable for improving AA profile of pea proteins. Natural variation among varieties for AA profile is present as depicted in Table 2. Wide crosses and mutants can be searched for more desirable AA profile of pea proteins. Furthermore, introgression approach can be deployed for improvement of existing germplasm using a natural variation.
\nAmino acids | \ncv. ucero\n | \ncv. ramrod\n | \ncv. agra\n | \n\ncv.terno\n | \n\ncv.Xantos\n | \n\ncv.suit\n | \n\ncv.achat\n | \n
---|---|---|---|---|---|---|---|
\nNon-essential amino acids\n | \n\n | \n | \n | \n | |||
Asp | \n10.39 | \n10.08 | \n9.98 | \n10.87 | \n10.55 | \n10.69 | \n10.58 | \n
Glu | \n17.09 | \n16.49 | \n15.43 | \n15.07 | \n16.19 | \n15.96 | \n16.16 | \n
Ser | \n4.89 | \n4.80 | \n4.77 | \n4.23 | \n4.16 | \n4.05 | \n4.25 | \n
Gly | \n8.16 | \n8.26 | \n7.85 | \n4.11 | \n4.0 | \n3.98 | \n3.92 | \n
Arg | \n5.76 | \n4.93 | \n4.12 | \n9.36 | \n8.60 | \n9.68 | \n8.32 | \n
Ala | \n5.17 | \n6.35 | \n5.75 | \n4.19 | \n3.88 | \n3.83 | \n3.79 | \n
Pro | \n3.62 | \n3.64 | \n3.52 | \n3.77 | \n3.57 | \n3.64 | \n3.63 | \n
\nEssential amino acids\n | \n\n | \n | \n | \n | |||
His | \n1.07 | \n1.13 | \n1.03 | \n2.22 | \n2.16 | \n2.18 | \n2.16 | \n
Val | \n3.85 | \n3.89 | \n3.61 | \n4.72 | \n4.29 | \n4.34 | \n4.32 | \n
Met | \n0.65 | \n0.70 | \n0.70 | \n5.0 | \n1.08 | \n1.05 | \n0.99 | \n
Cys | \n0.30 | \n0.37 | \n0.39 | \n2.01 | \n2.03 | \n1.9 | \n1.67 | \n
Ile | \n3.51 | \n2.64 | \n2.52 | \n4.23 | \n3.86 | \n3.77 | \n3.9 | \n
Leu | \n5.72 | \n6.51 | \n7.01 | \n7.11 | \n6.45 | \n6.33 | \n6.55 | \n
Phe | \n5.07 | \n5.06 | \n4.59 | \n4.87 | \n4.59 | \n4.33 | \n4.56 | \n
Tyr | \n3.98 | \n3.76 | \n3.77 | \n2.79 | \n3.18 | \n2.87 | \n3.18 | \n
Lys | \n18.34 | \n19.69 | \n17.03 | \n6.93 | \n6.55 | \n6.39 | \n6.63 | \n
Thr | \n3.04 | \n4.22 | \n6.92 | \n3.45 | \n3.64 | \n3.34 | \n3.53 | \n
Trp | \n0.02 | \n0.02 | \n0.02 | \nn.a. | \nn.a. | \nn.a. | \nn.a. | \n
Apart from protein comprising a major part of the seed, the other constituents include 1.5–2% fat, minerals, vitamins, polyphenols, oxalates, saponins and phytic acid [32, 33, 34]. Starch and dietary fibre account for 60 percent of carbohydrate content and rest include non-starch part of carbohydrates comprising sucrose, cellulose, and oligosaccharides (Figure 1) [34, 35]. Protein and the starch fraction of seed show high variations, whereas the other components remain comparatively constant [15]. Pea proteins are classified based on Osborne fractionation [36] into two different categories, i.e., globulins soluble in salt and albumins soluble in water which collectively account for 80% of the pea seed protein. Young embryos after germination of seed obtain nitrogen from globulins and some of the albumins which are also known as storage proteins. Globulins are further divided into two categories based on coefficients of sedimentation, i.e., legumin (11S fraction), vicilin and convicilin (7S fraction) as shown in Figure 2. The two classes differ from each other in structure and molecular weight. Legumin has a molecular mass ranging from 300 to 400 kDa and hexameric protein form. There are three polypeptide families of legumin, and sequence similarities differentiate them into various groups. The LegA polypeptide comprises of legA, legB, legA2, legC, and legE, LegJ polypeptide comprises leg J, legK, legL and legM whereas LegS is single member of family [37, 38]. The LegA and LegJ families comprise an apparent subdivision with the molecular mass of 65 kDa, and on the other hand, the apparent subdivision of LegS has *) kDa molecular mass. Only a single peptide of legumin is imported to the endoplasmic reticulum and removed during translation. Ultimately, trimers of legumin peptide are formed and moved to the pre-vacuolar compartment [39]. Furthermore, the peptides are processed into basic and acidic polypeptides of 20 and 40 kDa with the help of vacuole processing enzyme and the two peptides are linked by disulphide bridge. A complete protein structure is assembled from trimers to hexamers. The molecular weight of vicilin is 47–50 kDa and it forms trimers of 150 kDa molecular mass [40]. Only some vicilins undergo cleavage at post translational level [41]. Vicilin contains two cleavage regions which are separately processed. Three fragments of 13 kDa (â), 20 kDa (R) and 16 kDa (γ) are obtained by cleavage in both regions. Two fragments of 25 kDa (â + γ) and 20 kDa (R) are obtained, if site A is cleaved and two fragments of 16 kDa (γ) and 36 kDa (R + â) are obtained if site B is cleaved [41, 42]. Noncovalent bonds held processed peptides [38, 42]. Glycosylation takes place near to C terminus of γ subunit of vicilin polypepetides as they are glycosylated [43]. Trimers of 210 kDa molecular mass are formed by convicilin protein having a molecular mass of 70 kDa. Heteromeric trimers comprising convicilin and vicilin polypeptides also occur [2, 44]. Elimination of single peptide is only reported post translational modification in the case of convicilin and glycosylation is absent [45]. Convicilin and vicilin show sequence similarity of amino acids at C terminus whereas N terminal being highly charged have different sequences between two polpeptides [46, 47]. Based on isoform, sequence similarity occurs between 122 and 166 amino acid residues. Physiochemical properties of globulins are different, owing to variations in molecular weight and structure.
\nThe average composition of pea seeds [56].
Size of subunits of pea proteins, including the cleavage site of (a) Legumin (b) Vicilin (c) Convicilin [57].
The water-soluble albumin proteins have 5–80 kDa molecular mass and consist of enzymes and anti-nutritional factors such as amylase inhibitors, lectins and protease inhibitors [32]. Further two classes are obtained in albumins, i.e., albumin protein with two polypeptides having 25 kDa molecular weight and another with 6 kDa molecular weight [44]. Minor portions include prolamins which are soluble in diluted alcohol and glutenins, which are soluble in diluted acid [32]. The protein structure can be altered by external factors such as temperature, pH and salts during the extraction process resulting in different surface features and conformations.
\nThe globulin protein classes, i.e., vicilin and legumin in different concentrations, can make good gels, whereas convicilin is known to hinder gel formation [48]. The food industry needs raw material with desirable composition of globulin in peas like high levels of vicilin and legumin or low levels of convicilin [38]. Further, gel making property not only depends on the composition of globulins but also matter of isoforms of isolate [49, 50]. The genetic variation in the composition of globulins and decreased levels of anti-nutrients in albumin fraction of pea proteins are desirable material for development of new varieties using breeding techniques. Natural variation is reported in case of the protein content of pea and its composition, which can be used in breeding programs [51, 52, 53]. The r locus in the pea genome is known to control the starch synthesis, which shows pleiotropism with protein content and its composition [54, 55]. With the advancement of techniques for elucidating in planta processing of proteins, there will be more clues for the controlled composition of proteins using genome editing techniques.
\nAlkaline extraction/isoelectric precipitation (AE/IEP) – This method utilizes the high solubility of pea proteins in alkaline conditions and their minimal solubility at isoelectric point (pI) at pH between 4 to 5 [32]. This method is the most common for legume protein extraction, and it takes advantage of similar solubility characters for legumin and vicilin [33, 58]. The de-fatted flour of legume (with or without seed coat) is dispersed in water and pH is adjusted to an alkaline range using NaOH, KOH or Ca(OH)2, and further left for 30–180 mins for maximizing protein solubility [32, 33]. Without de-fatting process, the protein-lipid interaction limits the solubility of protein leading to decrease in the isolated yield, and the temperature can be increased to 50–60°C to aid solubilization [59, 60]. The protein denaturation can be limited by avoiding the higher temperatures. The mixture is further centrifuged, and supernatant is collected, and isoelectric pH is adjusted using HCl or H2SO4. The precipitated protein is collected after centrifugation and washed, neutralized, and dried by drum or freeze drying [32, 33]. The isolate yield can be increased up to 80–94% by optimal processing conditions and the conditions used in a process can affect the purity, yield and functionality of the isolate [58]. Hoang [58] evaluated that the extraction pH and flour: water ratio were most critical factors. The flour: water ratios of 1:5 to 1:20 (w/v) was reported [32] but Hoang [58] stated that the increase in concentration gradient between the solid and liquid phase in low ratio slurry can increase solubility. Although high alkalinity increases the isolate solubility and yield of protein, but the pH 11 and above are basically associated with increase in swelling of starch, leading to contamination of starch in isolate product [58]. Alkaline Extraction is also responsible for the adverse chemical reactions like the conversion of serine and cysteine residues to lysinoalanine compounds (nephrotoxic), decreased proteins bioavailability, and racemization of amino acids [61, 62]. The processes employing high alkaline pH, high temperature is associated with high yield of isolate, but there is high susceptibility of denaturation of isolate [61, 63]. The particle size of flour and solubilizing agent used can also affect the yield of isolate. The optimum particle size for flour is 100–150 μm and it was reported that NaOH and KOH generate more yield in comparison to Ca(OH)2 [64]. Also, there was protein loss of 6.2% from discarded supernatant from this extraction method [58] and in place of IEP, ultrafiltration (UF) or diafiltration membranes with specific molecular weight cutoffs can be utilized for isolating proteins of interest from the supernatant [32]. The efficiency of extraction can be improved by alteration in the molecular weight cutoffs, membrane type, concentration, and volume of the filtrate and addition of diafiltration to UF techniques [65]. The albumin proteins can be recovered by controlling these factors and further result in enhancing yield of isolate and alteration in isolate functionality leading to reduction in effluent losses. The use of UF can provide milder conditions for extracted proteins, so that their functionality can be enhanced and it gives higher yields in comparison to IEP [66].
\nBoye et al. [65] also confirmed that there were slightly higher protein levels in UF than the IEP process. Membrane filtration is also effective in reduction of anti-nutritional compounds in isolate [65]. Taherian et al. [67] conducted a study for functional properties of commercial and membrane-processed yellow pea protein isolates. The use of UF results in reduction of phytic acid upto 28–68% and possess improved functionality (e.g., solubility, rheology, foaming and emulsification) for commercially available isolates. The solubility of the commercial protein isolates was reported as ~20% vs. ~80% by using UF/diafiltration at pH 2.0. Fuhrmeister and Meuser [68] found the enhanced solubility, emulsifying, foaming and fat-holding properties by UF recovery of proteins from wrinkled pea relative to heat, acid, and heat/acid precipitation.
\nSE has advantage of the salting-in and out phenomenon of proteins which is followed by desalting for lowering the ionic strength of protein environment [32, 69]. In this process, the flour is stirred in salt solution of ionic strength (1:10 (w/v) ratio) for 10–60 mins and further followed by removal of insoluble matter by settling, screening, decanting, filtering or centrifugation. The supernatant is desalted and dried [32, 69, 70]. The choice or concentration of salts is selected according to salting-in and salting-out characteristics of the protein and any unwanted proteins, respectively because the proteins precipitate at an array of ionic strengths [71, 72]. The salting-in of proteins generally occurs at ionic strength (between 0.1 to 1 M) [60] and the other factors include interactions of salt and sample components and ensuring the use of food-grade salts [69, 73]. The major advantage for this technique is that extreme level of acidic or alkaline pH alongwith elevated temperature is not required. The extraction occurs at pH level of 5.5–6.5, but Crevieu et al. [74] reported slightly alkaline pH for increasing protein solubility [69]. The pH can be maintained by the addition of acid or base or a salt solution with buffering capacity can be used. The supernatant with extract of high-salt protein should have a protein concentration of 15 to 100 mg/mL [69] and many methods have been used for decreasing its ionic strength.
\nIn the process of micellization, protein precipitation is induced by adding cold water at a ratio of 1:3 to 1:10 (v/v) of high-salt protein extract to water [69, 75]. The solubilized proteins can be adjusted to low ionic strength by the dilution of protein solution through different dissociation reactions which forms loosely associated and low molecular weight aggregates. After reaching a specific concentration of protein, the aggregates can re-associate into low molecular weight species, known as micelles [69]. The arrangement of micelles is as thermodynamical spheres with minimum interfacial energy by giving exposure to polar moieties in outer aqueous environment and hydrophobic moieties towards the center. The proteins possesing more surface hydrophobicity have more protein–protein interactions and are also more successful for creating large and uniform aggregates [69]. The diluted solution can be left to stand for certain time for increasing micelle formation. This is followed by centrifugation and further the pellet is dried, and the high salt aqueous solution is discarded [32, 69]. Mwasaru et al. [75] reported that after using 0.25 M NaCl solution at pH value of 6.5 and 6 hours of micellization standing time, the protein extractability for pigeon pea and cowpea was yielded a 40.2% and 36.7%, respectively and these values were further compared to alkaline-extracted samples at pH value of 10.5 and 8.5, respectively, where the yields increased with respect to alkalinity. Gueguen [35] evaluated that 95% yield can be attained using micellization method.
\nThe another commonly used method for desalting is dialysis. It is the process of membrane separation driven by a potential gradient for diffusing water and other solutes with low molecular weight like, salt and this process carried out using semipermeable membrane [72]. Gueguen et al. [70] and Crevieu et al. [74] used pea protein membranes with cutoffs of 8000 Da and 12,000–14,000 Da, respectively. The diffusion requires time for causing equilibrium on both sides and is complete when the potential gradient becomes negligible [72]. The changes in fresh, precooled liquid against which the sample is dialyzed helps in ensuring that very low concentrations of solutes remain in the sample. Gueguen et al. [70] cited a process of 130 hours which requires five changes of water of 20 times the extract volume. Crevieu et al. [74] dialyzed solution of globulin against two changes of 10 times the extract volume of ammonium carbonate, that requires 70 hours and results in a yield of 66.8%. Dialysis can also be used for separation of gloulin and fractions. According to the protein classification of Osborne, the dialyzed sample is centrifugated and it results in dissolved albumin fractions in supernatant and precipitated fractions of globulin in the pellet [70]. The phenolic compounds present in pea can be reduced by additional steps during processing, like the use of alcohol washes and charcoal filters. The cross linkage of proteins can be improved by antioxidant activity of phenolic compounds which can negatively affect protein digestibility and enzymatic activity, leading to undesirable color and flavor compounds within the food product.
\nThe application of bioactive ingredients (hydrophobic, hydrophilic compounds, minerals, and probiotics) is less due to their instability, less bioavailability, and unsuitable flavors in the food system. So, encapsulation can be a promising technique for solving these problems related to bioactive ingredients. Nowadays, there is an increase in research for pea protein as encapsulating materials, because of its health benefits, nil genetic modifications, and hypoallergenic issues [76]. As many researchers have recognized the importance of natural polymers for preparing biodegradable packaging and since pea protein acts as a biodegradable and biocompatible natural polymer, it can be used for producing biodegradable films. It can provide promising possibility for the application of pea proteins for making biodegradable films in industrial-scale food production.
\nThere are extrusion techniques which include low-moisture extrusion (LME, 40%) and high-moisture extrusion (HME, >40%), these techniques are widely used in commercial food production. LME is generally used for preparation of snacks and HME is used basically for meat analogue preparation. The research of pea protein based extruded products is very common nowadays and many researchers reported that pea protein was used in different starches like rice starch [77, 78, 79] wheat starch [80] and corn grits [81] for preparing protein-fortified extruded snacks by LME, and the results concluded that pea protein-fortified extruded products exhibits high content of protein and possess balanced amino acid profile in comparison to pure extrudates of starch.
\nThere are many studies which report that by the addition of pea protein in cereal products can improve the nutritional value of the product because pea protein provides the essential amino acids and improve the texture of cereal product [4, 82, 83, 84, 85]. The plant protein can be used as substitute for animal protein for meeting nutritional need of lacto-vegetarians and thus can make the food healthier. Several researchers are working on partly or fully substitution of dairy proteins with pea protein and the impact on taste and structure of these products [86, 87, 88, 89, 90].
\nBased on the literature reviewed in this chapter, we think that analogous research and advancement on pea proteins would be required if any significant boost in pea protein utilization is envisaged. While pea protein isolates have usually been discussed in the research literature as relatively mundane, you will find very few sensory analysis information to help the claim. The main limitation on the sales of pea protein meals components is the trouble in fighting with the well-established, versatile soy protein items which dominate the meals protein market. Soy proteins are already available for a very long time, and research by the main producing businesses has resulted in several tailored items for programs. Pea concentrates and flours are generally referred to as having a terrible taste (beany, bitter). The incorporation of pea concentrates and flours into meals products such as bread, is usually restricted by flavour problems. This truth is insignificant within the foods ingredient industry because proteins in this particular marketplace are sold primarily by functional qualities and price. Although to be used in food aid plans for developing nations, this’s of concern and demands that pea protein is together with a protein source that will offer a comprehensive source of sulfur amino acids. In pet feeding, the nutritional value of protein sources is likewise essential. Feeding studies show that pea protein requires supplementation with methionine to get it with the nutritional value of soy protein.
\nIntechOpen - where academia and industry create content with global impact
",metaTitle:"Team",metaDescription:"Advancing discovery in Open Access for the scientists by the scientist",metaKeywords:null,canonicalURL:"page/team",contentRaw:'[{"type":"htmlEditorComponent","content":"Our business values are based on those any scientist applies to their research. We have created a culture of respect and collaboration within a relaxed, friendly and progressive atmosphere, while maintaining academic rigour.
\\n\\nCo-founded by Alex Lazinica and Vedran Kordic: “We are passionate about the advancement of science. As Ph.D. researchers in Vienna, we found it difficult to access the scholarly research we needed. We created IntechOpen with the specific aim of putting the academic needs of the global research community before the business interests of publishers. Our Team is now a global one and includes highly-renowned scientists and publishers, as well as experts in disseminating your research.”
\\n\\nBut, one thing we have in common is -- we are all scientists at heart!
\\n\\nSara Uhac, COO
\\n\\nSara Uhac was appointed Managing Director of IntechOpen at the beginning of 2014. She directs and controls the company’s operations. Sara joined IntechOpen in 2010 as Head of Journal Publishing, a new strategically underdeveloped department at that time. After obtaining a Master's degree in Media Management, she completed her Ph.D. at the University of Lugano, Switzerland. She holds a BA in Financial Market Management from the Bocconi University in Milan, Italy, where she started her career in the American publishing house Condé Nast and further collaborated with the UK-based publishing company Time Out. Sara was awarded a professional degree in Publishing from Yale University (2012). She is a member of the professional branch association of "Publishers, Designers and Graphic Artists" at the Croatian Chamber of Commerce.
\\n\\nAdrian Assad De Marco
\\n\\nAdrian Assad De Marco joined the company as a Director in 2017. With his extensive experience in management, acquired while working for regional and global leaders, he took over direction and control of all the company's publishing processes. Adrian holds a degree in Economy and Management from the University of Zagreb, School of Economics, Croatia. A former sportsman, he continually strives to develop his skills through professional courses and specializations such as NLP (Neuro-linguistic programming).
\\n\\nDr Alex Lazinica
\\n\\nAlex Lazinica is co-founder and Board member of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his Ph.D. in Robotics at the Vienna University of Technology. There, he worked as a robotics researcher with the university's Intelligent Manufacturing Systems Group, as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and, most importantly, co-founded and built the International Journal of Advanced Robotic Systems, the world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career since it proved to be the pathway to the foundation of IntechOpen with its focus on addressing academic researchers’ needs. Alex personifies many of IntechOpen´s key values, including the commitment to developing mutual trust, openness, and a spirit of entrepreneurialism. Today, his focus is on defining the growth and development strategy for the company.
\\n"}]'},components:[{type:"htmlEditorComponent",content:"Our business values are based on those any scientist applies to their research. We have created a culture of respect and collaboration within a relaxed, friendly and progressive atmosphere, while maintaining academic rigour.
\n\nCo-founded by Alex Lazinica and Vedran Kordic: “We are passionate about the advancement of science. As Ph.D. researchers in Vienna, we found it difficult to access the scholarly research we needed. We created IntechOpen with the specific aim of putting the academic needs of the global research community before the business interests of publishers. Our Team is now a global one and includes highly-renowned scientists and publishers, as well as experts in disseminating your research.”
\n\nBut, one thing we have in common is -- we are all scientists at heart!
\n\nSara Uhac, COO
\n\nSara Uhac was appointed Managing Director of IntechOpen at the beginning of 2014. She directs and controls the company’s operations. Sara joined IntechOpen in 2010 as Head of Journal Publishing, a new strategically underdeveloped department at that time. After obtaining a Master's degree in Media Management, she completed her Ph.D. at the University of Lugano, Switzerland. She holds a BA in Financial Market Management from the Bocconi University in Milan, Italy, where she started her career in the American publishing house Condé Nast and further collaborated with the UK-based publishing company Time Out. Sara was awarded a professional degree in Publishing from Yale University (2012). She is a member of the professional branch association of "Publishers, Designers and Graphic Artists" at the Croatian Chamber of Commerce.
\n\nAdrian Assad De Marco
\n\nAdrian Assad De Marco joined the company as a Director in 2017. With his extensive experience in management, acquired while working for regional and global leaders, he took over direction and control of all the company's publishing processes. Adrian holds a degree in Economy and Management from the University of Zagreb, School of Economics, Croatia. A former sportsman, he continually strives to develop his skills through professional courses and specializations such as NLP (Neuro-linguistic programming).
\n\nDr Alex Lazinica
\n\nAlex Lazinica is co-founder and Board member of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his Ph.D. in Robotics at the Vienna University of Technology. There, he worked as a robotics researcher with the university's Intelligent Manufacturing Systems Group, as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and, most importantly, co-founded and built the International Journal of Advanced Robotic Systems, the world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career since it proved to be the pathway to the foundation of IntechOpen with its focus on addressing academic researchers’ needs. Alex personifies many of IntechOpen´s key values, including the commitment to developing mutual trust, openness, and a spirit of entrepreneurialism. Today, his focus is on defining the growth and development strategy for the company.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). He is the member of many Pharmaceutical Associations and acts as a reviewer of scientific journals and European projects under different research areas such as: drug delivery systems, nanotechnology and pharmaceutical biotechnology. Dr. Sezer is the author of many scientific publications in peer-reviewed journals and poster communications. 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I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). 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