Production of cereals, number of tractors and population in the world’s least developed countries.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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Typically, pandemic ignitions occur due to zoonotic agents tied to long-term human impacts on biodiversity. The ongoing emergent and highly-contagious Chinese coronavirus SARS-CoV-2 that induces a syndrome misnamed Coronavirus disease-2019 (COVID-19) is one of the most recent zoonotic pathogens resulting from human impact on wildlife. While the real origin of the SARS-COV-2 virus is not known, we do know that specific wild animals, which were hosts of the virus, were illegally caught by poachers (Africa or Asia) and then transported and used in fake medicines, as food in restaurants, and sold to Chinese wet markets' clients, possibly resulting in the virus transfer to humans. Another possibility is that the virus, due to human error, was leaked from a lab close to the wet market in China. The nature of the original virus is not completely known. Indeed, it could be a native virus, resulting from a research misuse of these viruses to create new augmented viruses, which exhibit increased pathogenesis as compared with native wild type viruses. Here, we propose an updated integrated view of the COVID-19 pandemic.
\r\n\r\n\tThe virus's probable origin, associated controversies, socio-economic impacts, and failures in preparedness approaches, including the lack of wildlife preservation and restoration, will be discussed. Fundamental and applied knowledge is provided to understand the SARS-CoV-2 physio-pathogenesis in human populations and in animal models to explore and/or review new or repurposed therapies as well as different vaccine strategic options.
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Thereafter, he moved internationally and worked as a postdoctoral researcher at various hospitals and universities in Canada, that is, Sainte-Justine Hospital, University de Montreal, Montreal, Quebec (2009–2010); Queens’s University, Kingston, ON (2010–2012); and Sunnybrook Health Science Centre, University of Toronto, Toronto, ON (2012–2013). He worked with Translational Immunology Group at Trinity College, Dublin, Ireland (2014–2015). Currently, he is working with Children Health Clinical Unit, Faculty of Medicine at University of Queensland, Brisbane, Queensland, Australia. Dr. Kumar is the recipient of the prestigious 'Piero Periti Review Article Award” for the year 2008, awarded by the Journal of Chemotherapy in the field of immunomodulation and antimicrobials for the article entitled 'Innate Immunity in Sepsis Pathogenesis and Its Modulation: New Immunomodulatory Targets Revealed.” He is the recipient of junior research and senior research fellowship [JRF and SRF (2004–2009)] offered by the Indian Council of Medical Research (ICMR), New Delhi, India. Till now, he has published 40 publications in peer-reviewed international journals in this field. 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In brief, emergence occurs when a complex system shows qualitatively different properties then its building blocks. Numerous examples of emergent systems studied in contemporary condensed matter physics, including high‐temperature superconductors and heavy‐fermion compounds [2], are regarded as systems with spontaneous symmetry breaking [3]. A link between emergence and spontaneous symmetry breaking, however, does not seem to have a permanent character. In a wide class of electronic systems, such as semiconducting heterostructures containing a two‐dimensional electron gas (2DEG), physical properties of itinerant electrons are substantially different than properties of free electrons (or electrons in atoms composing the system), and are also highly‐tunable upon variation of external electromagnetic fields [4]. To give some illustration of this tunability, we only mention that electrons in GaAs heterostructures can be usually described by a standard Schrödinger equation of quantum mechanics with the effective mass
It is rather rarely noticed that graphene, a two‐dimensional form of carbon just one atom tick [7], also belongs to the second class of emergent systems (i.e., without an apparent spontaneous symmetry breaking) described briefly above. In a monolayer graphene, effective Hamiltonian for low‐energy excitations has a Dirac‒Weyl form, namely\n
where Strictly speaking,
A peculiar nature of Dirac fermions in graphene originates from the chiral structure of the Hamiltonian
Although the interest in graphene and other Dirac systems primarily focus on their potential applications [28, 29], quite often linked to the nonstandard quantum description [8], we believe that the fundamental perspective sketched in the above also deserves some attention. In the remaining part of this article, we first overview basic experimental, theoretical and numerical findings concerning signatures of quantum chaos in graphene and its nanostructures (Section 2). Next, we present our new numerical results concerning the additive random matrix model originally proposed in Ref. [25] to describe a nonstandard GUE‒GOE transition, accompanied by lifting out the valley degeneracy (Section 3). The consequences of these findings for prospective experiments on graphene nanoflakes, together with the phase diagram depicting the relevant matrix ensembles in the system size‐doping plane, are described in Section 4. The concluding remarks are given in Section 5.
\nDirac fermions confined in graphene quantum dots [30] have provided yet another surprising situation, in which a piece of handbook knowledge needed a careful revision [31].
\nQuantum chaotic behavior appears generically for systems, whose classical dynamics are chaotic, and manifest itself via the fact that energy levels show statistical fluctuations following those of Gaussian ensembles of random matrices [32]. In particular, if such a system posses the time‐reversal symmetry (TRS), its spectral statistics follows the Gaussian orthogonal ensemble (GOE). A system with TRS and half‐integer spin has the symplectic symmetry and, in turn, shows spectral fluctuations of the Gaussian symplectic ensemble (GSE). If TRS is broken, as in the presence of nontrivial gauge fields, and the system has no other antiunitary symmetry [33], spectral statistics follow the Gaussian unitary ensemble (GUE). For a particular case of massless spin‐1/2 particles, it was pointed out by Berry and Mondragon [34], that the confinement may break TRS in a persistent manner (i.e., even in the absence of gauge fields), leading to the spectral fluctuations of GUE.
\nWhen applying the above symmetry classification to graphene nanosystems [24, 25], one needs, however, to take into account that Dirac fermions in graphene appear in the two valleys,
Transitions between symmetry classes and random matrix ensembles relevant for
It is worth mention here, that triangular graphene flakes, similar to studied theoretically in Ref. [25], have been recently fabricated [37, 38]. However, due to the hybridization with metallic substrates, quantum‐dot energy levels in such systems are significantly broaden, making it rather difficult to determine the symmetry class via spectral statistics.
\nAdditive random‐matrix models are capable of reproducing the evolutions of spectral statistics in many cases when a complex system undergoes transition to quantum chaos or transition between symmetry classes [32, 39]. The discussion usually focus on the auxiliary random Hamiltonian of the form\n
where To describe transition to quantum chaos rather then transition between symmetry classes in a chaotic system, one can choose
For instance, if elements of
where
The above follows from the normalization condition\n
The limiting forms of the spacing distribution given by Eqs. (3) and (4) are\n
coinciding with well‐known Wigner surmises for GOE and GUE, respectively [32]. For
Relatively recently, spectra of models employing self‐dual random matrices have attracted some attention [41]. In such models, the matrix
where random matrix
In the remaining part of section, we focus on the transition between self‐dual GUE to GOE, show that the corresponding Hamiltonian
We focus now on the situation, when the matrix
For
where
The matrix on the right‐hand side of Eq. (10) is self‐dual, and can be further transformed as\n
where\n
Exchanging the second with the third row and column in the rightmost matrix in Eq. (11) we arrive to\n
where the blocks
Spectral statistics of the Hamiltonian
The nearest‐neighbor spacings distribution can be approximated by\n
with
and\n
Eqs. (15) and (16) represent simplified versions of the corresponding formulas given in Ref. [43]. A comparison with the numerical will be given later in this section.
\nWe consider now the case of large random matrices (
Ensembles of large pseudo‐random Hamiltonians
We find that nearest‐neighbor level spacings of large matrix
with the empirical relations of Eqs. (15) and (16) [see blue solid lines in Figure 3] now replaced by
\nLevel‐spacing distributions for
and\n
The above formulae are marked in Figure 3 with red dashed lines. We also find that the scaling law
Least‐squares fitted parameters of
Scaling law for the best fitted parameters
In this section, the empirical distribution
At zero magnetic field, the tight‐binding Hamiltonian for weakly‐disordered graphene can be written as\n
where
A model of substrate‐induced disorder, constituted by Eqs. (20) and (21), was widely used to reproduce numerically several transport properties of disordered graphene samples [45‒48]. Here, we revisit the spectra of closed graphene flakes considered in Ref. [25], within a simplified empirical model
A compact measure of the disorder strength is given by the dimensionless correlator\n
where the system area
For
The numerical results are presented in Figure 5, where we have fixed the remaining disorder parameters at The disorder parameters are actually same as in Figures 8 and 9 of Ref. [25], where we have mistakenly omitted the factor
where the total number of terminal sites\n
Eq. (25) is now employed to estimate the maximal system size
On the other hand, system size and the number of energy levels taken into account must be large enough to distinguish between spectral fluctuations of GUE and spectral fluctuations of other ensembles.
\nDensity of states (per one direction of spin) for bulk graphene reads\n
The number of energy levels
Physically, occupying
Level‐spacing distributions
where Eq. (33) refers to the empirical distribution We use the property of
where
where the factor
For
Phase diagram for triangular graphene nanoflakes with zigzag edges. Grey solid line in left panel (replotted as a dashed line in right panel) corresponding to
Limiting values of
We have revisited level‐spacing statistics of triangular graphene nanoflakes with zigzag edges, subjected to weak substrate‐induced disorder. Our previous study of the system is complemented by comparing the spectral fluctuations with these of large random matrices belonging to a mixed ensemble interpolating between GUE with self‐dual symmetry and generic GOE. The results show that for a fixed value of maximal Fermi energy
In conclusion, we expect that triangular graphene flakes with
The author thanks to Huang Liang for the correspondence. The work was supported by the National Science Centre of Poland (NCN) via Grant no. 2014/14/E/ST3/00256. Computations were partly performed using the PL‐Grid infrastructure.
\nCereals are essential for human nutrition. However, the ever-increasing world population makes it difficult to maintain the security of this food source. It is essential to overcome this situation by increasing cereal yield. An additional issue is the fact that while some countries suffer from hunger, a significant amount of food is discarded in others. This study analyses both production and consumption of cereals with the goal of ensuring food security.
\nOn the production side, many developing countries lack production efficiency mainly due to a lack of capital and human resources skills. First, we theoretically demonstrated a cereal production gap between developing and developed countries. Second, we performed empirical analysis to confirm the theoretical demonstration. We also clarified how the production and consumption status of three typical cereals (wheat, rice and maize—or corn in United States English) are related to the regional food cultures.
\nOn the consumption side, we investigated the cause of food loss and waste. We applied economic theories to demonstrate the market situation where food loss and waste are occurring. We then introduced the data relating to food loss and waste in G20 and the least developed countries in the world to interpret the current world situation. In addition, we also considered some cases of macroeconomic impacts on cereal productions (i.e. weather extremes and changes in commodity prices).
\nThe remainder of this chapter is organised as follows. Section 2 provides the formal definition of food security. Section 3 provides both the theoretical and empirical analysis of cereal production. Section 4 analyses the causes of food loss and waste. Section 5 interprets the additional issues of cereal production (i.e. the macroeconomic impacts) and Section 6 concludes the chapter.
\nAccording to the Food and Agriculture Organisation of the United Nations (FAO), food security exists when all people—at all times—have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life [1]. The issue of food security has been raised as a major agenda at the United Nations (for example, Goal 2 of the Sustainable Development Goals: End Hunger, Achieve Food Security and Improved Nutrition and Promote Sustainable Agriculture) [2].
\nThe above agenda is mainly due to the expected increase in the world population potentially make it difficult to ensure food security with the current level of food production. Overcoming this situation will require increasing the production capacity of nutritious foods for human consumption (especially wheat, rice and maize). However, the cereal production capacity varies across countries. In particular, there is more room for improvement in the crop yield in developing countries than in developed countries. Moreover, a significant amount of edible food is being discarded without being consumed in many developed countries whereas some developing countries concern food shortages. Eliminating such consumption waste can contribute to food security as much as increasing production capacity.
\nIn this section, we used a simple economic model to illustrate the problems of cereal production in developing countries and the methods for its improvement1. In order to highlight the problem of cereal production in developing countries, we comparatively analysed developing and developed countries. A farmer produces a product (i.e. cereals) by using labour and capital as demonstrated in Eq. (1).
\n\nEq. (1) takes the form of a Cobb–Douglas production function, where \n
Next, we investigated the relationship between labour and capital in production for further details. Specifically, by fixing the production of cereals at a certain level (i.e.\n
According to Eq. (2), \n
Next, we set the farmer’s budget constraints. To use labour and capital, wages and purchase/maintenance costs for capital resources were incurred, and the farmers are supposed to cover them out of their budgets. Eq. (3) shows the farmer’s budget constraints.
\n\n\n
By using Eqs. (1)-(3), we demonstrate the farmer’s cereal production regarding to its first-order conditions. The first-order condition is derived by solving the problem with a Lagrange multiplier (Eq. (4)).
\nwhere \n
To solve the problem, we first differentiate \n
By using the first-order conditions of Eqs. (5)-(7), we can derive the optimal level of \n
\nEq. (8) specifies the optimal number of labourers to produce cereals in country \n
\nEq. (9) specifies the optimal amount of capital resources to produce cereals in country \n
The optimal set of inputs \n
Optimal Set of Imputs in Developed Countries.
Optimal Set of Imputs in Developing Countries.
The figures are two-dimensional, with the number of labourers in country \n
As illustrated in \nFigures 1\n and \n2\n, due to the small budget, the optimal amount of both inputs in developing countries is less than in their developed counterparts.5 However, closer inspection reveals that the optimal amount of capital is relatively larger than the number of labourers in developing countries. This is due to the lack of skilled labourers and the fact that there is more room for productivity improvements through capital in the case of developing countries.6 Even though this is the optimum input situation based on the model, the situation in reality differs. In many developing countries, development support has not sufficiently progressed, and agriculture is often carried out manually.
\nAlthough we theoretically demonstrate the optimal input of cereal production in developing and developed countries, it does not reflect the actual situation. In this section, we investigate the empirical data to identify the discrepancies between the optimality condition (\n
The data employed for the empirical analysis was extracted from FAOSTAT.7 Specifically, the production quantity (Element code: 5511) and domestic supply quantity (Element code: 5301) of wheat (Item code: 2511), rice (Item Code: 2805) and maize (Item code: 2514) in the group of 20 (G20) countries and the least developed countries in 2018 (Year code: 2018) are extracted from a domain of new food balance (Domain code: FBS).8 The number of tractors (Element code: 5116 and Item code: 2455009) in the G20 and least developed countries in 2006 (Year code: 2006) is extracted from a domain of machinery (Domain code: RM).9 The population (Element code: 511 and Item code: 3010) in the G20 and least developed countries in year 2018 were extracted from a domain of annual population (Domain code: OA). The food losses (Element Code: 5123) of wheat, rice and maize in the G20 countries and the least developed countries in 2018 are extracted from the domain of new food balance. 10\n
\n\n\nTables 1\n and \n2\n in the Appendix show the domestic production quantity and the production quantity of wheat, rice and maize, the number of tractors used for agriculture and the population in the world’s least developed countries and the G20 countries. Close inspection of the tables reveals the problems related to cereal production and food security in developing countries.
\nCountry | \nWheat | \nRice | \nMaize | \nTractors | \nPopulation | \n|||
---|---|---|---|---|---|---|---|---|
Product | \nDomestic Supply | \nProduct | \nDomestic Supply | \nProduct | \nDomestic Supply | \n|||
Afghanistan | \n3613 | \n6995 | \n352 | \n626 | \n107 | \n165 | \n711 | \n37171.92 | \n
Angola | \n3 | \n1111 | \n10 | \n725 | \n2765 | \n2996 | \n\n | 30809.79 | \n
Bangladesh | \n1099 | \n6885 | \n54416 | \n56415 | \n3288 | \n4841 | \n\n | 161376.7 | \n
Benin | \n0 | \n186 | \n459 | \n2131 | \n1510 | \n1415 | \n\n | 11485.04 | \n
Burkina Faso | \n0 | \n277 | \n161 | \n523 | \n1700 | \n1616 | \n\n | 19751.47 | \n
Cambodia | \n0 | \n46 | \n10647 | \n10006 | \n604 | \n692 | \n\n | 16249.79 | \n
Central Africa | \n0 | \n5 | \n11 | \n11 | \n90 | \n86 | \n\n | 4666.368 | \n
Chad | \n2 | \n75 | \n260 | \n257 | \n438 | \n422 | \n\n | 15477.73 | \n
Djibouti | \n0 | \n339 | \n144 | \n241 | \n0 | \n4 | \n\n | 958.923 | \n
Ethiopia | \n4500 | \n6394 | \n54 | \n441 | \n8350 | \n8255 | \n\n | 109224.4 | \n
Gambia | \n0 | \n84 | \n2340 | \n214 | \n39 | \n42 | \n\n | 2280.094 | \n
Guinea | \n0 | \n420 | \n171 | \n3527 | \n819 | \n806 | \n\n | 12414.29 | \n
Haiti | \n0 | \n278 | \n183 | \n735 | \n260 | \n288 | \n\n | 11123.18 | \n
Laos | \n0 | \n24 | \n3585 | \n3436 | \n982 | \n737 | \n\n | 7061.507 | \n
Lesotho | \n9 | \n59 | \n258 | \n19 | \n100 | \n292 | \n\n | 2108.328 | \n
Liberia | \n0 | \n73 | \n4030 | \n598 | \n0 | \n1 | \n\n | 4818.973 | \n
Madagascar | \n6 | \n365 | \n112 | \n4774 | \n215 | \n222 | \n\n | 26262.31 | \n
Malawi | \n1 | \n145 | \n3168 | \n118 | \n2698 | \n2736 | \n\n | 18143.22 | \n
Mali | \n29 | \n341 | \n232 | \n3027 | \n3625 | \n3226 | \n\n | 19077.75 | \n
Mauritania | \n7 | \n551 | \n134 | \n295 | \n16 | \n23 | \n\n | 4403.313 | \n
Mozambique | \n21 | \n691 | \n27574 | \n1101 | \n1250 | \n1315 | \n\n | 29496 | \n
Myanmar | \n116 | \n560 | \n5152 | \n26176 | \n1984 | \n1559 | \n102750 | \n53708.32 | \n
Nepal | \n1958 | \n1961 | \n102 | \n5700 | \n2473 | \n2616 | \n\n | 28095.71 | \n
Niger | \n5 | \n66 | \n120 | \n309 | \n30 | \n60 | \n375 | \n22442.82 | \n
Rwanda | \n11 | \n158 | \n763 | \n148 | \n410 | \n207 | \n\n | 12301.97 | \n
Senegal | \n0 | \n682 | \n920 | \n2051 | \n264 | \n645 | \n\n | 15854.32 | \n
Sierra Leone | \n0 | \n91 | \n3 | \n1595 | \n23 | \n25 | \n\n | 7650.15 | \n
Sudan | \n595 | \n2754 | \n87 | \n125 | \n45 | \n63 | \n\n | 41801.53 | \n
Togo | \n0 | \n144 | \n145 | \n222 | \n887 | \n905 | \n\n | 7889.093 | \n
Uganda | \n23 | \n647 | \n246 | \n374 | \n2773 | \n2500 | \n\n | 42729.04 | \n
Tanzania | \n57 | \n984 | \n2220 | \n2134 | \n6273 | \n5206 | \n\n | 56313.44 | \n
Yemen | \n106 | \n3650 | \n0 | \n781 | \n43 | \n687 | \n\n | 28498.68 | \n
Zambia | \n114 | \n160 | \n43 | \n56 | \n2395 | \n2859 | \n\n | 17351.71 | \n
Production of cereals, number of tractors and population in the world’s least developed countries.
Note: \nTable 1\n is provided by the author based on the data extracted from FAOSTAT. Product and domestic supply of wheat, rice and maize are in 1,000 tonnes. Population is in 1,000 people.
Country | \nWheat | \nRice | \nMaize | \nTractors | \nPopulation | \n|||
---|---|---|---|---|---|---|---|---|
Product | \nDomestic product | \nProduct | \nDomestic product | \nProduct | \nDomestic product | \n|||
Argentina | \n18539 | \n5596 | \n1368 | \n753 | \n43462 | \n19237 | \n\n | 44361.15 | \n
Australia | \n20941 | \n8716 | \n635 | \n453 | \n387 | \n382 | \n\n | 24898.15 | \n
Brazil | \n5422 | \n12530 | \n11749 | \n12273 | \n82288 | \n64173 | \n\n | 209469.3 | \n
Canada | \n32216 | \n7940 | \n0 | \n533 | \n13885 | \n14215 | \n733182 | \n37074.56 | \n
China | \n131690 | \n127248 | \n214079 | \n206919 | \n257349 | \n277032 | \n\n | 1459378 | \n
France | \n35798 | \n19521 | \n73 | \n659 | \n12667 | \n8428 | \n\n | 64990.51 | \n
Germany | \n20264 | \n16289 | \n0 | \n405 | \n3344 | \n6714 | \n798700 | \n83124.42 | \n
India | \n99700 | \n95422 | \n172580 | \n142688 | \n27820 | \n23402 | \n\n | 1352642 | \n
Indonesia | \n0 | \n9868 | \n83037 | \n73805 | \n30254 | \n31380 | \n\n | 267670.5 | \n
Italy | \n6933 | \n11136 | \n1512 | \n699 | \n6179 | \n12034 | \n\n | 60627.29 | \n
Japan | \n766 | \n6700 | \n9728 | \n11232 | \n0 | \n15819 | \n\n | 127202.2 | \n
Mexico | \n2943 | \n6217 | \n284 | \n1352 | \n27170 | \n40514 | \n\n | 126190.8 | \n
Korea | \n26 | \n4663 | \n5195 | \n6688 | \n78 | \n10037 | \n\n | 51171.71 | \n
Russia | \n72136 | \n32416 | \n1038 | \n1175 | \n11419 | \n6447 | \n439600 | \n145734 | \n
Saudi Arabia | \n518 | \n3667 | \n0 | \n1882 | \n45 | \n3160 | \n\n | 33702.76 | \n
South Africa | \n1900 | \n3593 | \n3 | \n1406 | \n12510 | \n10510 | \n\n | 57792.52 | \n
Turkey | \n20000 | \n19134 | \n940 | \n1359 | \n5700 | \n7071 | \n1037383 | \n82340.09 | \n
The UK | \n13555 | \n16364 | \n0 | \n606 | \n0 | \n2067 | \n\n | 67141.68 | \n
The US | \n51398 | \n33837 | \n10153 | \n5504 | \n364262 | \n292878 | \n\n | 327096.3 | \n
Production of cereals, number of tractors and population in G20 countries.
Note: \nTable 2\n is provided by the author based on the data extracted from FAOSTAT. Product and domestic supply of wheat, rice and maize are in 1,000 tonnes. Population is in 1,000 people.
First, in many developing countries, both the quantity of production and the domestic production of cereals are far below those of the G20, which indicates that developing countries lack cereal production capacity. Moreover, many G20 countries’ production exceeds its domestic supply, indicating that those countries import cereals to cover shortages in their national consumptions. However, the opposite is true in the case of the least developed countries. A low level of production is indicative of threatened food security.
\nSecond, although based on scarce information, the number of tractors in the G20 countries is significantly higher than in developing countries. As demonstrated in the theoretical analysis, abundant machinery contributes to the production of cereals. The lack of machinery likely causes the difference in the productivity between developed and developing countries.
\nThird, the production of wheat, rice and maize varies according to the country’s region. For example, wheat production in many countries in \nTable 1\n is lower than that in countries in \nTable 2\n. This is because many developed countries are Western countries whose staple food is wheat (i.e. bread). On the contrary, many countries in \nTable 1\n produce more rice. This is because rice is often consumed in Asia and Africa, and those regions include many countries in \nTable 1\n. Unlike the other two cereals, maize is actively produced in the countries listed in both \nTables 1\n and \n2\n. This is because maize is often consumed in Africa as well as in Western countries.
\nTo make the discussion in 3.2.2 more reliable, we performed regression analyses. We confirmed the first (as compared to developed countries, developing countries lack cereal production capacity) and third argument (i.e. the production of wheat, rice and maize varies according to the country’s region).11\n
\nIn Eq. (10), the dependent variable \n
\n\nTable 3\n presents the descriptive statistics for the data employed for the regression analysis of Eq. (10). As mentioned earlier, the G20 production of all cereals far exceeds that of the developing countries. The difference is particularly noticeable in maize production and smallest of the three (wheat, rice and maize) in rice production. This is because there are many countries that have rice as their staple food in poor regions such as Southeast Asia.
\n\n | Wheat | \nRice | \nMaize | \n|||
---|---|---|---|---|---|---|
\n | The Least Developed Countries | \nG20 Countries | \nThe Least Developed Countries | \nG20 Countries | \nThe Least Developed Countries | \nG20 Countries | \n
Observation | \n165 | \n95 | \n165 | \n95 | \n165 | \n95 | \n
Mean | \n1056.667 | \n22955.14 | \n3775.121 | \n24536.86 | \n1343.521 | \n42863.25 | \n
Standard Deviation | \n1862.218 | \n30904.34 | \n10106.54 | \n54650.72 | \n1704.479 | \n83273.31 | \n
Minimum Value | \n5 | \n2735 | \n−42 | \n375 | \n1 | \n346 | \n
Maximum Value | \n7617 | \n127248 | \n56415 | \n206919 | \n8255 | \n343651 | \n
Descriptive statistics for \n
Note: \nTable 3\n is provided by the author based on the data extracted from FAOSTAT. \n
\n\nTable 4\n shows the estimation results of Eq. (10). Close inspection of the estimation results reveals some features that are consistent with the argument in 3.2.2. First, the estimated coefficients of \n
Independent Variable | \nDependent Variable \n\n | \nDependent Variable \n\n | \nDependent Variable \n\n | \n
---|---|---|---|
Estimated Coefficient | \nEstimated Coefficient | \nEstimated Coefficient | \n|
\n\n | \n24978.12*** (0.000) | \n32732.82*** (0.001) | \n45346.42*** (0.000) | \n
\n\n | \n17217.26*** (0.002) | \n31947.7*** (0.001) | \n44958.31*** (0.000) | \n
\n\n | \n7068.021*** (0.001) | \n8418.469*** (0.001) | \n80329.6*** (0.000) | \n
\n\n | \n24711.49*** (0.000) | \n54529.34*** (0.000) | \n46233.9*** (0.000) | \n
\n\n | \n13487.44*** (0.000) | \n254.84*** (0.002) | \n7352.68*** (0.000) | \n
\n | \n−17861.62*** (0.003) | \n−32457.05*** (0.002) | \n−46880.16*** (0.000) | \n
Number of Observations | \n260 | \n260 | \n260 | \n
Prob > F | \n0.000 | \n0.000 | \n0.000 | \n
R2\n | \n0.312 | \n0.309 | \n0.247 | \n
In order to comparatively analyse cereal production in the developed and developing countries in greater detail, in addition to Eq. (10), we conducted another estimation procedure. The production of cereals in a country depends on its population. By using per capita productivity, we can exclude the impact of the population. In order to reveal the latter issue, we set Eq. (11) as follows:
\n\nEq. (11) is different from Eq. (10) in that it uses \n
\n\nTable 5\n shows the descriptive statistics for \n
\n | Wheat | \nRice | \nMaize | \n|||
---|---|---|---|---|---|---|
\n | The Least Developed Countries | \nG20 Countries | \nThe Least Developed Countries | \nG20 Countries | \nThe Least Developed Countries | \nG20 Countries | \n
Observation | \n165 | \n95 | \n165 | \n95 | \n165 | \n95 | \n
Mean | \n0.0502536 | \n0.1482661 | \n0.1270291 | \n0.0540263 | \n0.0583697 | \n0.20179 | \n
Standard Deviation | \n0.1062425 | \n0.0939006 | \n0.162305 | \n0.0677363 | \n0.0550903 | \n0.2146537 | \n
Minimum Value | \n0.0010715 | \n0.0291697 | \n−0.0019442 | \n0.0045624 | \n0.0002075 | \n0.0146632 | \n
Maximum Value | \n0.8501159 | \n0.3557684 | \n0.61645 | \n0.2757308 | \n0.2365934 | \n1.063882 | \n
Descriptive statistics for \n
Note: \nTable 5\n is provided by the author based on the data extracted from FAOSTAT.
\n\nTable 6\n shows the estimation results of Eq. (11); the interpretation of these results requires careful consideration. In the case of wheat and maize, the coefficients of \n
Independent Variable | \nDependent Variable \n\n | \nDependent Variable \n\n | \nDependent Variable \n\n | \n
---|---|---|---|
Estimated Coefficient | \nEstimated Coefficient | \nEstimated Coefficient | \n|
\n\n | \n0.0388*** (0.001) | \n−0.150*** (0.000) | \n0.132*** (0.000) | \n
\n\n | \n−0.225*** (0.000) | \n−0.090*** (0.002) | \n0.177*** (0.000) | \n
\n\n | \n−0.211*** (0.000) | \n−0.014 (0.103) | \n0.399*** (0.000) | \n
\n\n | \n−0.209*** (0.000) | \n0.123*** (0.000) | \n0.132*** (0.000) | \n
\n\n | \n−0.066*** (0.000) | \n−0.011*** (0.000) | \n0.087*** (0.000) | \n
\n | \n0.270*** (0.000) | \n0.169*** (0.000) | \n−0.119*** (0.000) | \n
Number of Observations | \n260 | \n260 | \n260 | \n
Prob > F | \n0.000 | \n0.000 | \n0.000 | \n
R2\n | \n0.340 | \n0.403 | \n0.530 | \n
In the case of rice, looking at the estimated result of the regional dummies shows that only \n
Compared to \nTable 4\n, all the R2 values are slightly higher in \nTable 6\n. Therefore, the explanatory power of the estimation results improved slightly in \nTable 6\n.
\nIn Section 3, we describe both the theoretical and empirical analyses of the cereal supply. In this section, we identify the problems with and the methods for improving the consumption of cereals as food security issues. One might think that the only issue regarding food security is the improvement of production capacity; however, management of consumption is also important. Among many problems relating to cereal consumption, we focused on food loss and waste.
\nThe food situation differs from country to country. While some countries suffer from poverty and lack of food, others have excessive food supply in their markets and dispose of consumable foods.
\n\n\nTable 7\n shows the amount of food lost in the least developed and G20 countries.18 Not surprisingly, as compared to that in the least developed countries, the larger amount of food loss is reported in the G20 countries. Considering the larger production volume and the population, the loss in the G20 can be understood as a natural consequence of its food consumption. However, the developing countries are not completely devoid of food loss; although small comparatively, food loss is also reported in the least developed countries. However, reasons for food loss are different between the G20 and the least developed countries. In the case of the G20 countries, the cause of food loss is overstocking. For example, in Japan, consumers strongly demand food safety and quality [3]. As a result, foods that are not sold by the expiration date are discarded [3]. Moreover, consumable foods with slight scratches or incompatible size are discarded [3]. Another issue is that the main concern of business food suppliers is to make profit. When such economic agents compete with each other to form a market is a basis of capitalism. However, in order to make profits, they sometimes make decisions that are detrimental to society. For example, convenience stores in Japan often overstock foods, because they do not want to lose customers due to out of food stock [3].
\nThe Least Developed Countries | \nG20 | \n||||||
---|---|---|---|---|---|---|---|
\n | Wheat | \nRice | \nMaize | \n\n | Wheat | \nRice | \nMaize | \n
Afghanistan | \n542 | \n25 | \n16 | \nArgentina | \n316 | \n53 | \n633 | \n
Angola | \n0 | \n0 | \n307 | \nAustralia | \n209 | \n6 | \n2 | \n
Bangladesh | \n238 | \n3104 | \n250 | \nBrazil | \n247 | \n1181 | \n8321 | \n
Benin | \n0 | \n115 | \n378 | \nChina | \n2901 | \n8624 | \n11806 | \n
Chad | \n0 | \n10 | \n36 | \nFrance | \n326 | \n3 | \n112 | \n
Ethiopia | \n168 | \n3 | \n252 | \nIndia | \n5987 | \n4654 | \n2785 | \n
Madagascar | \n0 | \n403 | \n10 | \nItaly | \n49 | \n34 | \n11 | \n
Malawi | \n0 | \n5 | \n529 | \nJapan | \n163 | \n190 | \n4 | \n
Mali | \n21 | \n127 | \n218 | \nMexico | \n197 | \n53 | \n4571 | \n
Mauritania | \n22 | \n7 | \n1 | \nKorea | \n23 | \n481 | \n205 | \n
Mozambique | \n1 | \n5 | \n75 | \nRussia | \n433 | \n21 | \n115 | \n
Myanmar | \n24 | \n861 | \n90 | \nSaudi Arabia | \n35 | \n0 | \n92 | \n
Nepal | \n195 | \n486 | \n248 | \nSouth Africa | \n85 | \n0 | \n569 | \n
Niger | \n0 | \n4 | \n1 | \nTurkey | \n2133 | \n30 | \n202 | \n
Rwanda | \n0 | \n3 | \n49 | \nThe US | \n2334 | \n398 | \n17864 | \n
Senegal | \n6 | \n30 | \n33 | \n\n | |||
Sierra Leone | \n0 | \n120 | \n1 | \n||||
Sudan | \n48 | \n2 | \n4 | \n||||
Uganda | \n20 | \n5 | \n175 | \n||||
Tanzania | \n3 | \n30 | \n738 | \n||||
Zambia | \n3 | \n2 | \n72 | \n
The amount of food loss in the least developed and G20 countries.
Note: \nTable 7\n is provided by the author based on the data extracted from FAOSTAT. Food loss of wheat, rice and maize in the least developed and G20 countries are in 1,000 tonnes.
In the case of developing countries, due to a lack of sufficient capital for processing and preserving food, some foods are rarely delivered to consumers while fresh. For example, in developing countries, most of the postharvest grains are stored in traditional storage structures, which cannot prevent insect infestation and mould during storage [4].
\n\n\nFigure 3\n illustrates an excess supply of food (caused by overstocking) in developed countries.19 According to economic theory, if goods are traded at their market equilibrium (i.e. the intersection of the demand and supply curves), production and consumption match and no goods will be left unsold. However, as shown in \nFigure 3\n, if the price of goods deviates from its equilibrium, supply and demand of goods do not match, and some goods are not sold.
\nThe Market with Oversupply of Food.
\n\nFigure 4\n illustrates a food shortage situation in poor countries.20 Unlike in \nFigure 3\n, the supply curve in \nFigure 4\n becomes vertical at a certain quantity (\n
The Market with Shortage of Food.
In theory, eliminating oversupply of food and the assurance of food security in developing countries is mutually achievable. For example, transferring food surplus from developed countries to developing countries to combat the shortages would provide a solution to both problems.
\nAs discussed in Sections 3 and 4, there are the challenges to ensuring world food security both in food supply and consumption. On the supply side, the main reason for food insecurity is the lack of capital and training of human resources in developing countries. However, due to lack of funds, increasing capital and training human resources are hardly be achieved. Therefore, loans and assistance from international organisations and developed countries are required. Additionally, investment projects (i.e. foreign direct investment) are also effective in raising funds for developing countries. Furthermore, to gain an understanding of the outcome of such investments is larger in developing countries than in developed countries will be important21.
\nThere are other points that need improvement. First, improvement in seed breeding is effective for sustainable agricultural production [5]. Second, in many cases, as compared to farmers with large operations, smallholder farmers are disadvantaged in accessing market; therefore, farming should be re-invented as an attractive local business opportunity for smallholder families [6]. Third, in the agri-food system, a few large international firms dominate the market share, and smallholders mainly trade on local short-supply chains [7]. Fourth, in recent years, production and consumption of rice in many parts of Asia has been steadily declining, whereas the opposite is true in Africa [8]. Fifth, due to economic development, the demand for maize to feed livestock has increased [9]. Six, the problem of food self-sufficiency does not apply only to developing countries. In Japan, due to the ageing population and the lack of young people willing to engage in agriculture, the self-sufficiency rate is low [10]. As a result, Japan imports a substantial amount of food from abroad (i.e. China) to cover their shortages [10].
\nStabilisation of markets to protect suppliers from macroeconomic shocks is also important. For example, in 2020, the global economy was greatly affected by the spread of COVID-19. Because the main transmission route of COVID-19 is droplets from infected people, people stopped eating out. In Japan, food manufacturing and service businesses were severely damaged by people’s restraints and many food manufacturers and restaurants went bankrupt [11]. This was mainly because in Japan many restaurants are small and medium-sized enterprises that lack corporate strength to withstand severe economic adversities [12]. As a result, a significant amount of ingredients that were to be delivered to restaurants went left unsold. In addition, a sharp drop in the demand for restaurant food has spurred the overstock of food, leading to the collapse of the prices of some foods. If the situation does not improve, farmers will not be able to make sufficient profits and will be forced out of business. This will lead to a decline in food production capacity and will worsen food insecurity.
\nIn addition, inflation and instability of agricultural prices will put pressure mainly on the world’s low-income group of people and an increase in labour productivity will be necessary to overcome the problem [13]. The effect of extreme weather is also a major issue for cereal production. Weather extremes in the United States in 2012 caused a sharp increase in the world maize price, and the poor countries with high maize import dependency were the most seriously affected [14].
\nOn the consumption side, the main challenge is to reduce food loss in the developed countries. One measure to correct such market failure is government intervention in the market to internalise negative externalities. Additionally, transferring food surpluses from developed countries to developing countries to combat food shortages may improve the situation. Food banks are one of the potential measures for recycling food within a country [15]. If such food banks could be internationalised, food security in developed countries may be ensured in a way that is close to our idea. However, there are many difficulties with this potential solution. For example, some foods do not last long and are not suitable for long-distance transportation. Moreover, as discussed before, due to regional variances in food culture, some foods may be hard to replenish.22 Many countries also impose tariffs on imported products because large amounts of imported food may adversely affect local farmers’ businesses.
\nAdditionally, food loss also occurs in developing countries, where food security is not ensured. The main cause of food loss here is lack of capital for storage; therefore, as in the supply side discussion, increasing capital through investment and assistance is the effective measure23.
\nIn this section, we examined measures to improve food security. Although the authors’ proposed methods have the potential to improve food security, there are many challenges. In order to achieve these improvements, in addition to international efforts (i.e. international organisations and governments), changing the mindset of each economic entity (i.e. supplier and consumer) will be necessary.
\nCereals are essential for human nutrition. However, the ever-increasing world population makes it difficult to maintain food security. It is necessary to consider various factors related to production and consumption of cereals to ensure food security. This study considers the food security issues for cereals in terms of both supply and consumption.
\nOn the supply side, we proposed that cereal food security can be improved by increasing production capacity in developing countries. The optimal condition of our theoretical model specifies that, as compared to the case of developed countries, farmers in developing countries should more actively utilise capital resources. However, reality largely differs from theory. As shown by the data, the use of farming machinery in developing countries is lower than in developing countries. Additionally, our regression result confirms that the production of cereals (i.e. wheat, rice and maize) in G20 countries surpasses that in the least developed countries. Furthermore, our empirical analysis also confirms that the type of cereals produced vary significantly regarding the region.
\nOn the consumption side, we emphasised the problem of food loss. Although some countries in the world suffer from food insecurity, others discard large amounts of food. Reducing such waste can contribute to global food security. However, the causes of food loss differ between developing and developed countries. In the case of the former, the lack of capital means that food rots before it is consumed. In the case of the latter, foods are oversupplied to the market due to excessive competition between firms.
\nIn order to mitigate these problems, we proposed the following measures. In order to increase production in developing countries, their capital resources need to increased; this requires significant investment and assistance. On the other hand, one way to prevent food loss in developed countries is to mitigate excessive competition among firms through government market intervention. It may also be effective to transport surplus food from developed countries to developing countries to fill shortages. However, there are many challenges to implementing these measures. To truly improve the global food situation, in addition to international efforts (i.e. international organisations and governments), each of us needs to change our consciousness.
\nWe are deeply grateful for the editor, Dr. Aakash Goyal. We are also grateful to IntechOpen and Meikai University, which supported our research in various ways.
\nThese Terms and Conditions outline the rules and regulations pertaining to the use of IntechOpen’s website www.intechopen.com and all the subdomains owned by IntechOpen located at 5 Princes Gate Court, London, SW7 2QJ, United Kingdom.
',metaTitle:"Terms and Conditions",metaDescription:"These terms and conditions outline the rules and regulations for the use of IntechOpen Website at https://intechopen.com and all its subdomains owned by Intech Limited located at 7th floor, 10 Lower Thames Street, London, EC3R 6AF, UK.",metaKeywords:null,canonicalURL:"/page/terms-and-conditions",contentRaw:'[{"type":"htmlEditorComponent","content":"By accessing the website at www.intechopen.com you are agreeing to be bound by these Terms of Service, all applicable laws and regulations, and agree that you are responsible for compliance with any applicable local laws. Use and/or access to this site is based on full agreement and compliance of these Terms. All materials contained on this website are protected by applicable copyright and trademark laws.
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