\r\n\toxygen-free atmosphere. Biochar has been used for many years as a soil amendment and in general soil applications. Nonetheless, biochar is far more than a mere soil amendment. In this review, we report all the applications of biochar including environmental remediation, energy storage, composites, and catalyst production. In this book, we intend to collect contributions from worldwide experts in the field of biochar production and utilization providing a general overview of the recent uses of biochar in material science, thus presenting this cheap and waste-derived material as a high value-added carbonaceous source. Furthermore, we are aiming to give readers a handy and effective tool to easily understand how this field is interesting and diverse. It is a goal that this book could be easily used by any reader with a strong scientific background ranging from scientific company advisors to academic members. Nonetheless, students enrolled in scientific undergraduate and graduate programs could be consulted to this text for any further and deeper investigation. In the end, we intend to propose a very high scientific content book that could represent the reference text for any consideration and future study about biochar for the next years.
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\n
1. Introduction
\n
The roots of approximately 95% of vascular land plant species, with the exception of some families (e.g., the Brassicaceae, Amaranthaceae, and Polygonaceae), are colonized by symbiotic fungi which form a mutualistic relationship (mycorrhiza) with the host plant roots [1, 2]. Approximately 10% of vascular plant species, mostly woody species, are colonized by ectomycorrhizal fungi, which belong to the Basidiomycota, Ascomycota, and (less commonly) Zygomycota, and the fungal hyphae grow extracellularly, forming a mantle of mycelium around the roots [3, 4]. Most of the remaining mycorrhizal fungi, with the exception of family-specific mycorrhiza, such as the ericoid or orchid-specific mycorrhizal fungi, colonize nonwoody plant species and belong to the subphylum Glomeromycotina [3, 5]. This fungal group is generally known as the arbuscular mycorrhizal fungi (AMF), because these fungi form highly branched hyphal structure, known as arbuscules, in root cortical cells, and spread intercellularly (Arum-type) or intracellularly (Paris-type) [6]. The formation of arbuscules has been regarded as the unique morphological feature of this symbiosis responsible for the nutrient (particularly phosphorus) exchange between the host plant and the AMF [7]. Arbuscule formation occurs in parallel with the expression of a specific cellular system to allow the accommodation of AMF within the root tissue and to achieve nutrient (e.g., phosphorus and nitrogen) uptake via AMF mycelia [8, 9]. Genetic disruption of genes in the symbiotic system of model plants (e.g., Medicago truncatula, Lotus japonicus, Oryza sativa) has revealed the nutritionally beneficial relationships between plants and AMF [10, 11, 12].
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AMF can also colonize thalli of the early nonvascular land plants, namely, the liverworts and hornworts [13, 14]. Phylogenetic analyses indicated that symbiotic genes are present in the genomes of these early land plants, with the functions of the encoded proteins being conserved, suggesting that this symbiosis is phylogenetically widespread in plants [15]. AM symbiosis is beneficial for plants in relation not only to nutrition but also to the mitigation of biotic and abiotic stresses (e.g., resistance to pathogens, tolerance of drought and toxic element stress, increased biomass production, and secondary metabolite accumulation) [16, 17, 18, 19]. Hence, the functionality of AM symbiosis can influence the productivity and quality of crops.
\n
The effects on the host plant of AM symbiosis are commonly investigated by inoculation of the roots of the host plant with a (usually) single-species AMF in pot culture. However, field-grown inoculated roots can harbor AMF species other than the test AMF species, the functionality of which would not have been tested using an inoculation test because not all AMF in the roots can sporulate [20]. In addition, not all AMF which colonize roots are active and functional [21, 22], and the colonization process and the stability of the colonization by diverse AMF species in the same field-grown roots are unclear [23]. Overall, under field conditions, the functionality of AM symbiosis is probably based on unknown but highly dynamic associations between plants and a diverse range of AMF species. To better understand this complex association, in the current article, the colonization dynamics of AMF species in roots will be discussed. To understand the functional unit of the plant-AMF symbiosis, it is important to outline the concept of the colonization unit.
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\n
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2. Latent colonization dynamics in arbuscular mycorrhizas
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In 1905, an illustration by Gallaud showed that root cortical cells often contain “clumps” of arbuscules (Figure 1) [24]. Subsequent morphological examination of mycorrhizal roots at the cellular level suggested that this intracellular colonization may be ephemeral [25, 26, 27]. Following this, morphometric studies, coupled with electron microscopy, calculated that the life span of an intact arbuscule in several plant species was a minimum of 2.5 days [28, 29]. Such a limited life span of the units of intracellular colonization by AMF can be generalized because these arbuscule clumps have been observed in many plant species, including the relatively primitive nonvascular plant, the liverwort [13]. Why does this mutually beneficial association exhibit such short-lived units of intracellular colonization? Unfortunately, our understanding of this phenomenon has not increased much since the first observation of arbuscule clumps by Gallaud more than 110 years ago.
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Figure 1.
Arbuscule has a short life span. Image of sunflower (Helianthus annuus) mycorrhizal roots grown in field soil. 3,3′-diaminobenzidine (DAB) staining with horseradish peroxidase (HRP)—Wheat germ agglutinin (WGA) [65]. Bar = 50 μm.
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Advances in the forward and reverse genetic approaches available to decipher the molecular mechanism for the AMF colonization process have shown that several signaling mutants of model plants exhibited compromised AMF epidermal penetration and altered chemical and cellular crosstalk in the initial stages between plants and AMF [10, 30]. Recently, several mutant lines exhibited suppressed intraradical colonization, forming prematurely senescent or stunted arbuscules [11, 31]. Some plant genes have been implicated in the cellular process of arbuscule degeneration [32, 33, 34, 35]. The degeneration process of arbuscules has also been shown to be related to colonization level, illustrating the importance of arbuscule life cycle to the development of mycorrhiza. In addition, live imaging of the green fluorescent protein (GFP)—symbiotic phosphate transporter (PT11) fusion protein in the roots of mycorrhized rice seedlings—also revealed the limited life span of the units of intracellular colonization, in which the rapid collapse of arbuscules was observed [36]. The mechanism of mycorrhization, coupled with such a short life span of the individual colonization unit, has been addressed by continuous (long-term) live imaging of the symbiotic marker secretory carrier membrane protein (SCAMP) [37], where successive de novo colonizations underlie AM development (SubSection 2.2). These findings emphasize the importance of the life cycle of colonization when we consider the dynamics of AM functionality.
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2.1 Concept of the infection unit
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In soils, there are many different AMF species, and no strict host-AMF specificity has been observed [38, 39]. Accordingly, under field conditions, co-colonization of the same root by multiple AMF species can occur [2]. It is likely that the functionality of these diverse AMFs is not the same, and colonization by each AMF may last only a short time. Therefore, to correctly characterize the functionality of field mycorrhizas, the colonization process, the dynamics, and the functionality of the diverse AMFs in the roots need to be understood.
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When the fungal spore germ tubes approach the root surface, chemical crosstalk occurs between the roots and the AMF hyphae, triggering the molecular and cellular remodeling process necessary for hyphal entry into the roots [11]. Coinciding with this pre-symbiotic crosstalk, AMF hyphae around the roots are often highly branched, giving rise to a characteristic cascade-like mycelium, composed of lateral branches [2]. Several plant mutants exhibiting disruption of the early signaling process fail to allow hyphal penetration of the epidermal layer [10, 11], suggesting the importance of this initial mutual recognition process.
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After hyphopodia are formed, hyphae penetrate the rhizodermal layer and grow longitudinally in the root cortex. Short branches from the longitudinally extending hyphae penetrate the cortical cell walls and branch dichotomously in the cell lumen to give rise to arbuscules. Importantly, the maximum elongation in the cortex of hyphal structures derived from a single or a few hyphal penetrations of the epidermis are reported to be up to 20 mm [2]. In rice seedlings, however, the maximum elongation in the cortex of hyphal structures derived from the entry point is only 0.5 mm [37]. In general, the area occupied by each colonization unit derived from a single epidermal entry is difficult to recognize, because intraradical hyphae derived from different entry points immediately overlap one another to form a larger colonization area within the cortex. The rates of growth of intraradical colonization are reported to range from 0.13 to 1.22 mm/day [2]. In the live imaging of mycorrhizal rice roots, the rate varied from 0.42 to 1.68 mm/day [36]. Although the maximum length of intraradical colonization derived from one or a few penetrations varied greatly among studies, these independent colonies of mycelia are called the “infection unit” (Figure 2) [25].
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Figure 2.
The relationship between infection unit and mycorrhizal development. (A) Image of Lotus japonicus seedlings grown in field soil taken at 14 days post plantation. 3,3′-diaminobenzidine (DAB) staining with horseradish peroxidase (HRP)—Wheat germ agglutinin (WGA) [65]. Bar = 200 μm. (B) Model diagram of mycorrhization process. AMF spores germinate in the soil, and germ tubes approach the root surface and form a hyphopodium. The young infection unit, comprising an internal mycelium arising from one hyphal entry, grows and develops new arbuscules at the infection fronts. In many cases, new infection units develop immediately adjacent to established infection units. Colonized regions that were bound by two infection fronts, comprising intercellular and intracellular hyphae, develop through the successive formation of infection units. Arbuscules collapse from near the hyphopodia as a result of their short life span.
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2.2 Development and functionality of mycorrhizas: an infection unit-based view
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The development of the mycorrhiza and the dynamics of the infection unit are tightly linked processes during mycorrhization. It is also likely that the functionality of mycorrhiza in the field is also variable, depending on the functionality of the infection unit that is derived from different AMF species. Live in situ imaging revealed that multiple infection units overlap to form a larger infection (“colonized region”) within a few days [36, 37, 40], making the delimitation of each infection unit experimentally difficult. Coupled with the difficulty of differentiating between different AMF species in the roots, using morphological or molecular approaches [41, 42], the dynamic process of mycorrhization has probably hampered progress in characterizing the infection unit-based functionality of field mycorrhizas. Live imaging of rice mycorrhizal roots revealed that the expansion of the colonized region occurs in concert with successive de novo formation of multiple infection units (Figure 2) [37].
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However, a better understanding of the dynamics of the mycorrhization process depends substantially on the quality and the ease of imaging of the hyphae within the roots. Traditionally, cytological studies of the colonization process have been performed with chemical staining of fixed (i.e., dead) AMF structures or in situ visualization of their enzymatic activities. In recent studies, however, imaging of non-fixed root samples by means of fluorescent molecular markers of the symbiotic process has been used to improve our understanding of colonization dynamics at the cellular level. As many parts of the molecular mechanism implicated in AM symbiosis are common to nodule symbiosis, several molecular markers are available in the model legume plants, M. truncatula and L. japonicus [30]. However, live imaging of mycorrhizas in model legumes by means of fluorescent markers is difficult due to the presence of highly autofluorescent materials in the root tissue and the presence of thick (multiple) cortical cell layers that decrease transparency [43]. Such poorly transparent root tissues are not particularly useful for macroscopic imaging of the dynamics of the infection unit.
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Rice (O. sativa) is commonly grown in paddy fields, where it is rarely colonized with AMF. However, rice roots are colonized with AMF under semidry conditions. The root systems of leguminous species have cambium, and the diameters of primary and lateral roots are generally uniformly large. On the other hand, grass roots do not have cambium, and lateral roots are smaller in diameter than the primary (crown) root. In rice seedlings, lateral roots with a few cortical layers are the main site of AMF colonization, and the average diameter of roots is less than 200 μm [36, 44]. In addition, the concentrations of autofluorescent root materials are quite low, and some symbiotic molecular markers are available for live imaging [36, 37, 45].
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Transcriptome analysis of mycorrhized rice roots revealed that an AM-specific marker gene of rice, AM42, which encodes a SCAMP, is specifically expressed in mycorrhized roots [46, 47]. A GFP-tagged SCAMP protein was localized in the endomembrane systems of colonized cells and even in cells with collapsed arbuscules, allowing live imaging, coupled with GFP-SCAMP, to evaluate the colonization and recolonization sequences. Live imaging of GFP-SCAMP revealed that the average lifetime of intact arbuscules was 1–2 days. Cortical cells with collapsed arbuscules were rarely recolonized, whereas new colonizations occurred in close proximity to cells containing collapsed arbuscules, contributing to the expansion of the colonized region. Collapsed arbuscule-containing cells are intact [2]; however, colonization spread readily into an uncolonized region of roots but sparsely into a previously colonized region, suggesting that successive formation of new infection units is required for continuous mycorrhization [37, 48]. It is unlikely that the collapse and the presumed digestion of arbuscules play a significant role in nutrient transfer from fungus to plant [2].
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The concept that mycorrhization is linked to the successive formation of infection units is supported by the observation that decreased hyphopodium formation leads to decreased mycorrhization. Under low-phosphate conditions, roots secrete strigolactones (SLs), which are carotenoid-derived phytohormones. A chemical analog of SLs, GR-24, activated mitochondrial respiratory activity and facilitated hyphal branching of Gigaspora margarita or G. rosea under in vitro conditions [49, 50, 51]. In SL biosynthesis-defective rice mutants, the hyphal branching of a model AMF, Rhizophagus irregularis, around the roots (rhizospheric hyphal branching) was normal [48]; however, in the SL biosynthesis-defective mutants of pea, tomato, and rice, the percentage root length colonization was significantly reduced [52, 53, 54, 55]. In the rice SL biosynthesis mutants, the formation of the hyphopodium was delayed, compared with the wild type, but intraradical colonization was normal, indicating that the early formation of infection units, initiated by timely hyphal entry into epidermal cells, is necessary for the normal development of a colonized region [48].
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In the field, multiple AMFs, from different species, can co-colonize roots, with multiple AMF species being detectable in only a 1-cm-long root fragment [56]. Thus, field roots can be regarded as a mosaic of the various functionalities of the different AMFs [57]; alternatively, only a portion of the AMF infection unit colonizing the roots may temporarily contribute to particular functions in response to specific environmental conditions [58]. However, the dynamics of the functionality of the respective AMFs in field roots have been little studied. Abiotic and biotic factors may influence the AMF composition, at least over a long time period [59], but the short-term effects of such environmental factors may also influence the various active (functional) AMFs as well as the functionality of the mycorrhiza as an entirety under field conditions. For example, not all infection units containing fine-branched arbuscules in roots grown in field soils are metabolically active (Figure 3) [60]. Further study will be needed to understand the functional dynamics of field mycorrhizas by considering infection unit-based colonization dynamics.
\n
Figure 3.
Not all infection units revealed with fungal cell wall staining are metabolically active. Roots colonized with native AM fungi in field soils were subjected to cell wall (chitin) staining and vital staining to detect the presence of AMF and metabolically active AMF, respectively [60]. Vital staining, which histochemically visualizes the activity of succinate dehydrogenase (SDH), a tricarboxylic acid cycle enzyme in AM fungi, using the reduction of nitroblue tetrazolium (NBT) into insoluble formazan, detects metabolically active colonization [66]. The number of infection units detected by vital staining was lower than that determined by cell wall staining. In this analysis, rice (Oryza sativa L.) was used as the host plant because (i) the morphology of the development of infection units is well understood [36, 37]; (ii) active infection units rarely coalesce in roots [37], probably due to the small number of cortical cell layers [67, 68]; and (iii) the vital staining is convenient for detecting a single infection unit [36, 44].
\n
\n
\n
\n
3. Conclusion
\n
Our understanding of the molecular mechanism underlying the plant nutrient uptake system has been greatly advanced by the use of molecular studies and genetics. These studies have been based almost entirely on the plant alone. However, the plant nutrient uptake system is largely dependent on the largely undiscovered functionalities of diverse soil microorganisms. Roots of plants in the field are generally colonized with a range of different AMFs, but the functionality of these individual species is largely unknown. In addition, the genetic structure of AMF is quite enigmatic [41]. Recent studies into single-nucleus sequencing of some AMF culture lines demonstrated the presence of not only homokaryons but also dikaryons and heterokaryons [61, 62]. Furthermore, long-read whole-genome sequencing of R. irregularis DAOM197198 indicated that the genome contained 10 different rDNA sequences that were scattered (i.e., non-tandem repeats) around the chromosome [63]. These findings mitigate against the use of rDNA sequences to identify individual AM species in field AMF infection units, as a one-to-one relationship may not be applicable to the rDNA sequences and the genetic identities of the individual component species.
\n
As mentioned before, AM infection units have a short life span and collapse within a few days, at least in the live imaging of rice seedlings [36, 37]. The development of mycorrhizal roots is associated with the turnover and de novo colonization by new AMFs, providing the opportunity to allow different AMF species to colonize the roots, depending on the context (environmental factors, plant growth stage, nutrition, etc.). Accordingly, field mycorrhizal roots can comprise multiple functionalities with different AMF species.
\n
Plant breeding programs are not able to select for the genomic properties of plants adapted to all field conditions (soil type, water content, nutrient level, climate, etc.). On the other hand, plant roots closely interact with the native fungal partners that may have genetically recorded beneficial traits for adapting to the local environment. The specific phenotype (functionality) of native AMFs may be conferred by accessory genes that are not shared by all members of a species [64]. However, due to the complexity of the genetic basis of AMF individuals, it is difficult to understand which AMF genes are really functional in the roots. In a model plant (e.g., rice), the thin root cortex would allow us to isolate the genetic information of AMF individuals in the form of the infection unit. Furthermore, rice roots are technically suitable for detecting metabolic activities by means of vital staining. Furthermore, transgenic rice producing fluorescent molecular markers (e.g., phosphate transporter, GFP) is available to assess the functionality of the AMFs in situ. Thus, future studies should focus on the functionality of field AMF individuals, with emphasis on the genetic information and the dynamic functionality.
\n
\n
Acknowledgments
\n
This work was supported partly by the Japan Science and Technology Agency [ACCEL grant No. JPMJAC1403].
\n
Conflict of interest
The author declares that they have no conflict of interest.
\n',keywords:"arbuscular mycorrhiza, functional molecular markers, infection unit, life cycle, live imaging",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/67736.pdf",chapterXML:"https://mts.intechopen.com/source/xml/67736.xml",downloadPdfUrl:"/chapter/pdf-download/67736",previewPdfUrl:"/chapter/pdf-preview/67736",totalDownloads:968,totalViews:0,totalCrossrefCites:3,totalDimensionsCites:3,totalAltmetricsMentions:9,impactScore:1,impactScorePercentile:64,impactScoreQuartile:3,hasAltmetrics:1,dateSubmitted:"February 21st 2019",dateReviewed:"May 22nd 2019",datePrePublished:"June 19th 2019",datePublished:"November 20th 2019",dateFinished:"June 19th 2019",readingETA:"0",abstract:"Most land plant species have their roots colonized by arbuscular mycorrhizal fungi (AMF). These symbiotic associations are often found in the roots of field crops. The biological basis and practical significance of this symbiosis have been extensively studied, and the molecular mechanisms underlying the initial colonization process and the nutrient exchange between the host plant and the AMF have been elucidated. However, developmental processes and turnover of elements of the mycorrhiza, and the resulting changes in mycorrhizal function, are not well understood. The enigmatic nature of the development-function relationship is probably due to the short life span of the infection unit, which has largely been overlooked in studies investigating mycorrhizal function at the macroscopic level. This paper outlines the concept of the infection unit and functional expression patterns in terms of the transient aspects of the micro-symbiont during its life cycle in this symbiosis.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/67736",risUrl:"/chapter/ris/67736",book:{id:"8044",slug:"root-biology-growth-physiology-and-functions"},signatures:"Yoshihiro Kobae",authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Latent colonization dynamics in arbuscular mycorrhizas",level:"1"},{id:"sec_2_2",title:"2.1 Concept of the infection unit",level:"2"},{id:"sec_3_2",title:"2.2 Development and functionality of mycorrhizas: an infection unit-based view",level:"2"},{id:"sec_5",title:"3. Conclusion",level:"1"},{id:"sec_6",title:"Acknowledgments",level:"1"},{id:"sec_9",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Trappe JM. Selection of fungi for ectomycorrhizal inoculation in nurseries. 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Proceedings of the National Academy of Sciences of the United States of America. 2015;112:6754-6759. DOI: 10.1073/pnas.1504142112\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Yoshihiro Kobae",address:"kobae@rakuno.ac.jp",affiliation:'
Laboratory of Crop Nutrition, Department of Sustainable Agriculture, Rakuno Gakuen University, Ebetsu, Hokkaido, Japan
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1. Introduction
Cassava is a major staple food and widely grown across Nigeria owing to its wide adaptability, economic importance, and acceptance both in rural and urban regions being a common staple consumed by Nigerians. It is also increasingly becoming raw materials for food, feed, and industrial applications. In 2018, worldwide production of cassava stood at about 278 million tons (t). In the same period, Nigeria produced about 60 million t [1], and Africa’s total production was about 170 million t (about 56% of the world production) [1]. It is a source of calcium, vitamins B and C, and other essential minerals [2]. However, several biotic and abiotic constraints, such as drought, pests, diseases, low soil fertility, shortage of planting material, postharvest physiological deterioration, and access to markets, limit cassava production [3, 4].
A major impact of climate change is drought or water deficit, which imposes limited water environment on plants [5]. Global monitoring and analysis of climatic variables have provided evidence that the countries where cassava is cultivated are experiencing impacts of climate change [6]. Under drought conditions, water available for plant uptake for metabolic reactions falls below requirement, thus adversely impacting growth and physiological processes. The effects of water deficit on cassava plants are many and vary depending on length and intensity of drought and stage of growth of the plant [7].
Drought or water deficit remains the major impact of climate change, which imposes limited water environment on plants and seriously affected tuber yield [5]. Crops are dependent on rainfall, and so water scarcity is the primary productivity constraint in arid and semiarid tropical areas [8]. As a meteorological event, drought is a period in which the potential evaporation exceeds the rainfall. Agricultural drought is the result of water flow imbalance between the environmental demands of evapotranspiration and water transport in the soil-root system [9]. Water stress increases abscisic acid (ABA) concentration in plant, which in turn increases root resistance by affecting membrane permeability and root tuberization (Figure 1) [10, 11].
Figure 1.
General description of physiological responses of plants to drought stress conditions. ROS: Reactive oxygen species and ABA: abscisic acid [12, 13].
The morphophysiological responses to drought stress increases abscisic acid (ABA) concentration, ion transport, and the induction of the associated signaling pathway genes in plant, which in turn increases root resistance by affecting membrane permeability and root tuberization [14]. Under water deficit, cassava leaves rapidly accumulate large amounts of ABA and young leaves halt leaf expansion growth and transpiration rate decreases. Young leaves accumulate more ABA than mature leaves, but the high ABA levels under water deficit are completely reversed to control levels after one day of re-watering, corresponding with a rapid recovery of leaf area growth rate. The rapid reduction in leaf area growth and stomatal closure might be due to cassava’s ability to rapidly synthesize and accumulate ABA at an early phase of a water deficit episode [15]. Plants have developed defense mechanisms, which enable them to adapt and survive under drought condition in their life cycle [16].
1.1 Biochemical and molecular mechanisms known to contribute to water-deficit stress tolerance in cassava plants
The defense strategies against drought environment also vary from different cassava cultivars. During a prolonged drought stress condition, reactive oxygen species (ROS) generate excessively and cause oxidative damage [17]. ROS can damage multiple cellular components such as proteins and lipids, and unlimited disruption will finally lead to cell death [18].
1.2 Use of crop diversity in plant breeding for drought-tolerance traits
Valuable genes from natural inter- and intraspecific diversity can be used to take advantage of several mechanisms of survival and coadaptation in plants produced by natural selection [19]. Some of these genes are conserved by farmers (in landraces) or are present in crop wild relatives and the narrow genetic base of modern cultivars; therefore, crop wild relatives have been extremely valuable in adapting crop varieties to changing climatic conditions [20].
Cassava grows and produces well in the Nigerian environment but shows different growth behavior and yields in different years due to differences in annual weather conditions. The water regime of an environment is an essential factor that affects the growth of crops. Differential soil water and nutrient regimes have been reported to affect yield stability in cassava [21]. Although it is incredibly tolerant to water stress, a long dry period has been reported to decrease yields [22]. Similarly, prolonged moisture deficiency leads to a reduction in growth, development, and root yields [23].
In the past, decade the International Institute of Tropical Agriculture (IITA) had developed improved varieties, which were selected from diverse source crosses; that are resistant to the crops, major pests, and diseases. As these new genotypes are developed, there is need to evaluate their performance under different moisture regimes to identify those that are stable across varying moisture environments. This study evaluates the vegetative growth parameters and root yield of selected genotypes under different moisture conditions in the screen house and three different agro-ecologies. The objectives of the study are to determine the influence of soil moisture stress on vegetative growth and root yield of selected cassava genotypes.
2. Materials and methods
Twelve genotypes (IITA-TMS-IBA010040, IITA-TMS-IBA011086, IITA-TMS-IBA011663, IITATMSIBA020131, IITA-TMS-IBA30572, IITA-TMS-IBA91934, IITA-TMS-IBA920067, IITA-TMS-IBA920326, IITA-TMS-IBA950166, IITA-TMS-IBA980510, IITA-TMS-IBA980581, and TMEB 1) were selected based on their field performance for root yield, root numbers, and dry matter and were evaluated in the screen house at IITA, Ibadan. The plants were raised in large polythene bags of 36 cm length and 156 cm circumference for 6 months under three moisture conditions: 75% (control), 50%, and 25% F.C. using the procedure of Anderson and Ingram [24].
The F.C. moisture levels at 75% (well-watered), 50% (moderate), and 25% (severe) were used to simulate stress conditions in the field. The polythene bags were filled with 86 kg of topsoil (obtained from Ibadan) to a height of 36 cm and made firm by being doubled. The soil used was classified as Ferric Luvisol with sandy-loam texture (USDA); pH (water) was 5.4, organic carbon (C) 1.26%, and total nitrogen (N) 0.12%. Available phosphorus (P) was 34.4 mg/kg, calcium (Ca) 5.8 cmol/kg, and magnesium (Mg) 0.7 cmol/kg. The experiment was laid out in a 3 by 12 factorial arrangement (moisture level × clones) in a completely randomized design (CRD) and replicated three times. Each of the replicates had a total of 36 bags in three rows of 12 bags per row. Healthy stakes of uniform length (25 cm) were planted vertically in the central portion of the bags. Holes at the bottom of the bags allowed easy draining. Following the procedure of Anderson and Ingram [24], plants were watered to field capacity for the first 4 weeks to ensure good plant establishment, after which moisture treatments were imposed by irrigation once a week with 5.58 liters for 75% F.C., 3.72 liters for 50% F.C., and 1.86 liters for 25% F.C. Measurements on vegetative traits were taken at 4, 8, 12, 16, and 20 weeks after planting (WAP) for plant height (cm), and stem girth (cm). Yield parameters were taken at 24 WAP.
These 12 genotypes were also evaluated on the field using healthy stakes of 25 cm length planted in a slanting position on ridges 30 cm high with two-thirds of the length buried in the soil. Mokwa [(Southern Guinea Savanna (SGS), Lat.9°291N and Long. 5°041E and 152 masl] and Zaria [Northern Guinea Savanna (NGS), Lat.11°111N and Long.11°781E and 610 masl]. The three locations represent different agro-ecologies with varying climatic and soil characteristics. Planting was done in each location when soil moisture was sufficient to sustain establishment. Weeding was manual at 1 month after planting (MAP), and herbicides were applied at 3, 6, and 9 MAP. Harvesting was done at 12 MAP.
2.1 Data collection
Data collected in the screen house were Plant height at 4, 8, 12, 16, and 20 WAP; Stem girth at a uniform stem length of 50 cm from ground level; Fresh shoot weight was obtained in kg as the fresh weight of shoots per plot; Fresh root weight per plot and estimated in t/ha at 24 WAP; Number of stems per plant; Number of leaves per plant was counted per plot and Screening genotypes for resistant to Cassava Mosaic Disease (CMD), Cassava Bacteria Blight (CBB), and Cassava anthracnose (CAD). The incidence and severity of genotypes to the African cassava mosaic disease (CMD) were evaluated at 1, 3, and 6 months after planting (MAP) at 3 and 6 MAP for cassava bacterial blight (CBB) and CAD at 6 and 9 MAP; since the symptom would not have expressed at 1 month after planting. Disease incidence was taken as proportion of plants units that are visibly diseased relative to total number of plants, while disease severity = volume of plant parts affected compared with the whole plant unit.
Disease incidence = Number of infected plants/plot/Total number of plants/plot.
Severity of genotypes to CMD was recorded based on a scale of 1–5 [25].
Leaf Chlorophyll: The leaf chlorophyll contents of four selected leaves in each plot were measured at 4, 8, 12, 16, and 20 WAP using Chlorophyll Meter Model SPAD-502 (Minolta Co. Ltd. Japan).
Leaf stomata conductance: A steady-state porometer (Licor Instrument Corporation, Model Li-1600) was used to measure diffusive resistance and transpiration rate on the abaxial surface of the uppermost fully expanded leaves of four plants per plot. The sensor head with a narrow leaf aperture (LI 1600–01) with an area of 1cm2 was used. Measurement was taken when sun was not too low or high in the morning (900–1100 h) and afternoon (1330–1530 h) on a clear sunny day at 3, 6, and 9 months after planting (MAP) in all locations [26].
The dried sample was weighed, and root dry matter percentage was calculated as follows.
Percent root cortexDM=dryweight/Fresh weight×100
Gari production: Cassava roots were converted to fermented roasted granules called Gari. Ten kilograms of roots were taken from each genotype harvested, washed with water, and grated with a grating machine. The pulp was put in a jute bag, and pressure was exerted on it to remove water from the pulp. Dewatering took up to 3 days and the pulp was also undergoing fermentation while being dewatered. The pulp was sieved to remove chaff and toasted in a pot until gelatinized grains were formed.
2.2 Statistical analysis
All data obtained were analyzed using the Statistical Analytical System (SAS) (9.2 version). The model used was the factorial arrangement in a CRD. Least Significant Difference (LSD) at 5% probability was used to separate mean squares.
3. Results
3.1 Performance of selected genotypes in different moisture conditions in the screen house and field environments
Mean squares (MS) from the combined analysis of variance (ANOVA) for root and shoot characters of the twelve cassava genotypes in the screen house at Ibadan are presented in Table 1. The result shows highly significant (p < 0.001) mean squares (MS) for all sources of variations. However, MS for all traits studied were significant for more than one source of variations. The mean squares (MS) for treatment were highly significant so also mean square for except for fresh root weight and fresh shoot weight. Whereas the interaction between replicate and the treatments was not significant except for fresh shoot weight as well as the interaction between treatment and genotype except for leaf chlorophyll. But the mean square for replicate was not significant for all the traits studied. Mean values at 75% and 25% FC differ significantly for all parameters at 24 WAP, except for leaf number, leaf chlorophyll content, and fresh root weight.
Sources of variation
DF
Plant height
Stem girth
Fresh Root weight
Leaf number
Fresh Shoot weight
Stem number
Leaf chlorophyll
Rep(R)
2
84.15ns
0.02ns
14.81ns
127.86ns
399.98ns
0.18ns
7.95ns
Trt (T)
2
16492.35***
0.44***
16336.57***
3412.78***
19628.18***
3.13**
505.89***
Rep×Trt
4
722.37ns
0.01ns
302.34ns
39.16ns
3367.64*
0.65ns
5.03ns
genotype
12
2758.90***
0.08**
642.83ns
245.47**
1593.05ns
2.60***
27.10***
Trt × genotype
22
311.85ns
0.05ns
449.85ns
92.22ns
1610.94ns
0.63ns
12.77*
Error
65
408.34
0.04
530.91
88.86
1227.67
0.43
6.57
R2
0.74
0.54
0.59
0.68
0.58
0.66
0.80
Table 1.
Pooled analysis of variance for yield and yield related traits of 12 cassava genotypes evaluated in the screen house Ibadan Nigeria.
***, **, * means significant at p ≥ (0.001, 0.01, 0.05) and ns means not significant.
When mean values at 25% FC were compared with mean values at 75% FC, it was observed that mean plant height decreased by 29.33%, stem girth by 17.32%, root weight by 61.76%, and shoot weight by 29.27% at harvest (Table 2). There was no significant difference in mean values at 75% and 50% FC for plant height at harvest 24WAP. Similar growth pattern was observed for plant height and stem girth over time at 75% and 50% FC (Table 3).
Plant
% diff.
Stem
% diff.
Root
% diff.
Shoot
% diff.
height
girth
weight
weight
TRT
(cm)
(cm)2
(Kg)
(kg)
75%FC (T3)
144.98
1.27
0.068
0.164
50%FC (T2)
127.93
11.7
1.2
5.5
0.044
35.3
0.142
13.41
25% FC (T1)
102.46
29.33
1.05
17.32
0.026
61.76
0.116
29.27
Table 2.
Mean and percentage differences of 12 cassava genotypes in the greenhouse at different moisture levels for different traits at 24WAP.
% diff = (T3-T1/T3) *100.
Treatment
pltht
stmgrth
stmno
lfno
lfchlorph
rtfrhwt
shtfrhwt
High water treatment (75%)
145.0a
1.3a
1.7a
43.3b
33.4b
68.7a
116.5b
Low water treatment (25%)
102.5b
1.1b
1.7b
30.8c
39.3a
26.3c
142.1ab
Medium water treatment (50%)
127.9a
1.2a
2.2a
50.1a
40.4a
44.1b
164.6a
Table 3.
Mean differences of 12 cassava genotypes in the screen house at different moisture levels for different traits.
Same numbers are not significant from each other while numbers together mean they are over lapse and a separate number is significantly different.
IITA-TMS-IBA010040 had the highest fresh root weight of 70.2 g, followed by IITA-TMS-IBA920326(55.4 g) and IITA-TMS-IBA980581 had fresh root weight of 50.9 g; while IITA-TMS-IBA30572 had the least fresh root weight of 38.4 g (Table 4).
clone
mpltht
mstmgrth
mlfno
mlfchlorph
mrtfrhwt
mshtfrhwt
mstmno
Genotype mean
010040
109.3
1.2
37.9
37.3
70.2
140.4
1.5
56.8
011086
108.6
1.1
39.0
39.9
41.3
107.1
1.4
48.3
011663
149.4
1.2
34.8
38.4
40.9
142.9
1.2
58.4
020131
133.2
1.2
43.2
34.8
41.9
146.8
1.7
57.5
30572
144.0
1.2
46.9
39.7
38.4
145.8
1.8
59.7
91934
102.8
1.0
52.0
38.8
45.2
151.6
2.6
56.3
920067
140.4
1.1
36.9
38.3
41.0
131.0
1.7
55.8
920326
106.1
1.1
48.4
38.0
55.4
150.4
3.2
57.5
010034
102.9
1.2
42.3
37.1
42.3
141.9
2.0
52.8
980510
121.8
1.4
42.0
38.9
44.1
126.2
1.6
53.7
980581
141.4
1.2
36.9
34.1
50.9
152.2
1.9
59.8
TME 1
141.6
1.3
36.8
37.3
44.8
156.3
1.8
60.0
Mean
125.1
1.2
41.4
37.7
46.4
141
1.9
56.4
SE
5.3
0
1.6
0.5
2.6
4
0.2
2.0
Min
102.8
1
34.8
34.1
38.4
107.1
1.2
45.6
Max
149.4
1.4
52
39.9
70.2
156.3
3.2
67.5
CV(%)
16.2
17
22.8
6.8
49.7
24.8
35.1
24.6
Pr. > F
***
*
**
***
ns
ns
***
LSD(0.)
24.25
0.24
11.31
3.08
27.65
42.05
0.79
7.9
Table 4.
Overall means of yield and yield related traits of 12 cassava genotypes evaluated in the screen house at 24 WAP.
***, **, * means significant at p ≥ (0.001, 0.01, 0.05) and ns means not significant.
Mpltht: mean plant hight, mstmgrth: mean stem girth, mlfchorph: mean leaf chlorophyll, mrtfrhwt: mean root fresh weight, mshtfrhwt: mean shoot fresh weight, mstmno: mean stem number.
The mean reduction in genotypes performance at moisture stress levels of 25% and 50% FC ranged from 15.9% (30572) to 44.3% (010040) for plant height, between 7.7% (020131) and 41.6% (920326) for stem girth, between 40.8% (011086) and 85.7% (30572) for root weight, and between 12.8% (TMEB 1) and 63.9% (011086) for shoot weight. The mean plant height was 125.1 cm with 011663 having the tallest while 980510 and 91934 being the shortest 102.8 cm. Stem girth ranged from 1 cm (91934) to 1.4 cm (980510); leaf number ranged from 34.8(011663) to 52 cm (91934). The coefficient of variation for the traits was quite low (Table 5).
Genotypes
Plant Height
Stem girth
Root weight
Shoot weight
(cm)
(cm2)
(Kg)
(Kg)
T1
T2
T3
% diff.
T1
T2
T3
% diff.
T1
T2
T3
% diff.
T1
T2
T3
% diff.
010040
76.5
114.1
137.3
44.3
0.9
1.3
1.3
30.8
0.055
0.049
0.105
47.6
0.114
0.14
0.166
31.3
011086
85.5
122.7
117.6
27.3
0.9
1.3
1.3
30.8
0.04
0.041
0.042
40.8
0.056
0.109
0.155
63.9
011663
127.7
142.6
177.9
28.2
1
1.3
1.2
16.7
0.02
0.044
0.058
65.5
0.103
0.152
0.172
40.1
020131
107.5
142.4
149.8
28.2
1.2
1.2
1.3
7.7
0.02
0.042
0.062
67.7
0.121
0.152
0.166
27.1
30572
129.5
148.6
154
15.9
1.1
1.2
1.2
8.3
0.01
0.034
0.07
85.7
0.145
0.111
0.181
19.9
91934
83.6
100.8
124.1
32.6
1
0.9
1.1
9.1
0.025
0.035
0.074
66.2
0.099
0.186
0.169
41.4
920067
110.8
155.5
154.7
28.4
1
1
1.3
23.1
0.022
0.034
0.066
66.7
0.091
0.138
0.162
43.8
920326
87.1
106.4
124.7
30.2
0.7
1.4
1.2
41.6
0.024
0.07
0.071
66.2
0.139
0.142
0.169
17.8
010034
88.8
92.8
127.2
30.2
1
1.1
1.4
28.6
0.023
0.031
0.072
68.1
0.116
0.142
0.166
30.1
980510
104.3
121.1
139.8
25.4
1.3
1.3
1.5
13.3
0.027
0.033
0.071
70
0.08
0.129
0.168
52.4
980581
107.5
160.1
156.6
31.4
1
1.3
1.2
16.7
0.019
0.072
0.06
68.3
0.135
0.155
0.165
18.2
TMEB1
120.7
127.9
176
31.4
1.2
1.2
1.4
14.3
0.027
0.038
0.068
62.3
0.143
0.16
0.164
12.8
Means
102.46
127.93
144.98
1.05
1.2
1.27
0.03
0.04
0.07
0.12
0.14
0.16
SE
5.19
6.37
5.76
0.04
0.04
0.04
0.003
0.003
0.004
0.009
0.005
0.003
LSD
25.88
35.3
39.29
0.31
0.64
0.25
33.06
48.12
33.01
64.76
34.65
71.4
Table 5.
Overall mean performance and percentage difference among 12 cassava genotypes under different moisture conditions in the greenhouse for four traits.
3.2 Overall fields disease means scores, yield, and yield-related traits of 12 cassava genotypes evaluated for 2 years at three locations in Nigeria
There was significant (P ≤ 0.05) difference in genotypes performance with respect to stay green, mean diseases score, fresh root yield, harvest index, root yield and size, shoot weight, and gari yield except for dry matter content. 80% of tested genotypes performed better than the checks in terms of stay green, mean diseases score, fresh root yield, and shoot weight while 70% and 30% of the genotypes outperformed checks with respect to root size and gari weight. The genotypes with outstanding field performance across the three locations were IBA980581, IBA010034, and IBA010040 (Table 6).
Genotypes
S.G.
mean diseases score
FYLD(t/ha)
HI
DM (%)
nrt
rtsz
htwt (kg)
Gari wt (kg)
Rank_Total
Final rank
IITA-TMS-IBA980581
2.5(8)
1.97(7)
22.32(1)
0.5
32(1)
86.7(4)
5.9(2)
32.5(1)
1.7(2)
21
1
IITA-TMS-IBA010034
3(3)
2(8)
21(3)
0.5
32(1)
88(3)
5.8(4)
30.8(2)
1.9(1)
31
2
IITA-TMS-IBA010040
2.2(12)
1.87(5)
21.6(2)
0.5
29(8)
76.7(6)
5.9(2)
29(6)
1.4(4)
34
3
IITA-TMS-IBA920067
2.3(11)
1.67(1)
17.4(10)
0.5
31(3)
76.1(7)
5.6(5)
24.4(11)
1.2(9)
48
4
IITA-TMS-IBA020131
2.7(5)
1.9(6)
18.07(7)
0.4
31(3)
68.4(10)
5.4(9)
30.7(3)
1.5(3)
48
5
IITA-TMS-IBA011086
2.7(5)
1.73(2)
18.68(5)
0.5
27(10)
95.7(1)
5(12)
30.2(4)
1.2(9)
50
6
IITA-TMS-IBA980510
2.5(8)
1.80(3)
18.05(8)
0.5
27(10)
82.5(5)
5.3(10)
27.5(8)
1.1(11)
55
7
IITA-TMS-IBA91934
3(3)
2.4(12)
18.13(6)
0.5
30(5)
75.4(8)
6.2(1)
25.4(10)
1.4(4)
55
8
IITA-TMS-IBA920326
2.6(7)
2.07(9)
19.6(4)
0.5
29(8)
73.3(9)
5.6(6)
28.5(7)
1.3(8)
57
9
IITA-TMS-IBA30572
3.1(2)
2.23(11)
17.65(9)
0.5
30(5)
88.6(2)
5.1(11)
25.8(9)
1.4(4)
62
10
IITA-TMS-IBA011663
2.5(8)
1.83(4)
15.02(11)
0.4
24(12)
61(11)
5.6(7)
29.6(5)
1.1(11)
64
11
TMEB1
4.7(1)
2.2(10)
13.48(12)
0.5
30(5)
53.8(12)
5.5(8)
20(12)
1.4(4)
75
12
Mean
2.8
1.9
18.3
0.5
29
74.3
5.6
27.8
1.4
Min
2.2
1.67
13.48
0.4
24
53
5
20
1.1
Max
4.7
2.4
22.32
0.5
32
95.7
6.3
32.5
1.7
SE
0
0.06
0.67
0
3
3
0.1
0.98
0.05
CV
14
16.5
28.8
59
13
26.8
17
27.3
27.6
Pr. > F
*
***
***
**
ns
***
***
**
Table 6.
Overall disease mean scores, yield, and yield-related traits of 12 cassava genotypes evaluated for 2 years at three locations in Nigeria.
***, **, * means significant at p ≥ (0.001, 0.01, 0.05) and ns means not significant, Number in parenthesis represents the rank/position of each genotype, S.G.: Stay-green, FYLD: Fresh root yield, nrt: number of roots harvested, rtsz: root size, shtwt: shoot weight, HI: harvest index,
3.3 Morphological and physiological field performance of 12 cassava genotypes evaluated at three locations in Nigeria for 2 years
There is significant difference (p ≥ 0.05) in genotypes morphological and physiological traits except for stem girth. More than half of tested genotypes perform better than checks in all traits except for stomata, level of branching, and number of leaves. The genotypes that recorded highest and least score for plant height were (IBA920067, IBA91934), number of nodes (IBA980510, IBA91934), chlorophyll content (IBA011663, IBA980581), and Leaf Area (IBA011663, TME 1). Genotypes IBA011663, IBA980510, IBA010040, IBA010034, IBA30572 were identified for being stable across three locations for physiological and morphological traits (Table 7).
Genotypes
Pltht (cm)
Stem girth (cm)
Nnode
Stomata
Chlorophyll content
Lbrch
Nleaf
LA(m2)
Rank_ Total
Final Rank
IITA-TMS-IBA920067
100(4)
8.7(1)
40.6(11)
125(6)
36.7(4)
1.2(10)
34(12)
0.01(6)
54
7
TMEB1
103(8)
7.7(4)
41.7(10)
138.7(1)
34.6(9)
1.1(12)
36.7(11)
0.01(6)
61
12
I IITA-TMS-IBA920326
94.2(10)
7.5(7)
45(6)
127.8(4)
35.5(5)
1.2(10)
38.4(10)
0.01(6)
58
9
IITA-TMS-IBA980581
105(6)
7.6(6)
45(6)
132.6(2)
31.1(12)
1.4(8)
40.6(9)
0.01(6)
55
10
IITA-TMS-IBA91934
87.3(12)
6.3(12)
38.2(12)
127.5(5)
37.3(2)
1.8(3)
41(8)
0.01(6)
60
11
IITA-TMS-BA980510
109(3)
7.7(4)
50.5(1)
123.5(7)
37.3(2)
1.8(3)
49(2)
0.01(6)
28
1
IITA-TMS-IBA30572
92.1(11)
8.6(2)
44.4(9)
120.5(10)
35(7)
2(1)
49.3(1)
0.01(6)
47
2
IITA-TMS-IBA010034
110(2)
7.4(9)
46.8(4)
120.2(11)
35(7)
1.7(6)
48.3(4)
0.012(4)
47
2
IITA-TMS-IBA010040
104(7)
7.4(9)
48.8(2)
122.1(9)
33.4(10)
1.8(3)
48.8(3)
0.012(4)
47
2
IITA-TMS-IBA020131
109(3)
7.5(7)
45.1(5)
114.4(12)
33.2(11)
1.7(6)
43.5(7)
0.013(3)
54
7
IITA-TMS-IBA011086
132(1)
6.9(11)
44.5(8)
123.2(8)
35.4(6)
1.9(2)
48.1(5)
0.014(2)
42
6
IITA-TMS-IBA011663
109(3)
7.8(3)
47.7(3)
132.4(3)
38(1)
1.2(9)
45.1(6)
0.015(1)
28
1
Grand Mean
105
7.59
44.9
125.7
35.21
1.6
43.6
0.01
Min
87.3
6.3
38.2
114.4
31.1
1.1
34
0.01
Max
132
8.7
50.5
138.7
38
2
49.3
0.02
SE
2.97
0.15
0.97
2.42
0.55
0.1
0.85
0
CV
18.2
18.97
12.1
14.4
6.85
20.5
17.67
51.9
Pr. > F
*
ns
**
*
***
*
***
*
Table 7.
Overall means morphological and physiological traits of 12 cassava genotypes evaluated for 2 years in three locations in Nigeria.
***, **, * means significant at p ≥ (0.001, 0.01, 0.05) and ns means not significant, Number in parenthesis represents the rank/position of each genotype Nnode: number of nodes, Lbrch: level of branching, Nleaf: number of leaves, LA: leaf area.
3.4 Location means recorded by 12 genotypes evaluated for 2 years at three locations in Nigeria
Ibadan location recorded the highest score for level of branching, chlorophyll content, plant height, gari weight, number of leaf, fresh root yield, harvest index with least score for cassava bacteria blight severity (CBBS), Mokwa location had highest mean score for shoot weight, number of nodes, stem girth, stomata, and least mean score for cassava anthracnose disease severity (CADS) while Zaria location recorded highest mean score for stay green, dry matter content, and least mean score for cassava mosaic disease severity score (CMDS). Zaria and Mokwa location recorded the same mean score for Harvest Index while Ibadan and Mokwa location also recorded the same score for root size (Table 8).
Traits
Location means
Across mean
Ibadan
Mokwa
Zaria
Level of branching
2 (1)
1.6 (2)
1.4 (3)
1.60
Chlorophyll
36.5 (1)
36.1 (2)
33.5 (3)
35.40
Stomata
120.7 (3)
133.7 (1)
129.1 (2)
127.80
Stem girth
4.4(2)
14.3 (1)
3.4 (3)
7.30
Plant height (cm)
134.1 (1)
115.1 (2)
68.5 (3)
105.90
Number of nodes
49.7 (2)
54.6 (1)
29.9(3)
44.70
Gari weight (kg)
2.1 (1)
0.9 (3)
1.5 (2)
1.50
Number of leaves
86 (1)
29.7 (2)
24.5 (3)
46.70
CMDS
1.6 (3)
1.4 (2)
1.1 (1)
1.40
CBBS
1.8 (1)
2.1 (2)
2.6 (3)
2.20
CADS
2.2 (2)
0 (1)
2.3 (3)
1.50
Fresh root yield (t/ha)
28.9 (1)
20.3 (2)
8 (3)
19.10
Harvest index
0.6 (1)
0.5 (2)
0.5 (2)
0.50
Shoot weight (kg)
34 (2)
35.7 (1)
12.2 (3)
27.30
Stay-green
2.7 (2)
2.7 (2)
2.9 (1)
2.80
Root size
6 (1)
6 (1)
5 (3)
5.70
Dry matter content (%)
29.7 (2)
26.7 (3)
33.9 (1)
30.10
Number of roots
96.8 (2)
97.7 (1)
42.4 (3)
79.00
Table 8.
Overall mean location values for morphological, physiological, and yield traits at three locations for 2 years in Nigeria.
3.5 The correlation coefficients for traits measured for 2 years at three locations in Nigeria
Most of the traits show significant correlation inter se. Notably, Fresh root yield was positively correlated garri, harvest index, and cassava mosaic disease, Fresh root yield was however negatively correlated to cassava bacterial blight (−0.28) and cassava anthracnose disease (−0.29).
Chlorophyll content was positively correlated to fresh root yield (0.25), but negatively correlated to dry matter (−0.14).
Stomata had significantly negative correlation with harvest index (−0.11) and number of root (−0.15).
Shoot weight correlated with chlorophyll content (0.19), and fresh root yield (0.79) but had negative correlation with dry matter (−0.17) (Table 9).
mcbbs
Mcmds
Mcads
chlrphy
Stomata
stgth
pltht
Nnode
Nleaf
Gari wt
FYLD
HI
DM
shtwt
Rtno
Rtsz
mcbbs
1
−0.34***
−0.14**
−0.14***
−0.01***
−0.02ns
−0.31***
−0.15***
−0.57***
−0.39***
−0.28***
−0.23***
0.27***
−0.19***
−0.06ns
1
mcmds
−0.32***
1
0.19***
0.03ns
0.01ns
−0.05ns
0.18***
0.02ns
0.52***
0.29***
0.13***
0.12**
−0.24***
0.08ns
−0.02ns
−0.32***
mcads
0.19***
−0.06ns
1
−0.049ns
−0.455***
0.19***
−0.03ns
−0.54***
0.29***
0.01ns
−0.29***
−0.01ns
−0.05ns
−0.21***
−0.28***
0.19***
chlrphy
0.09ns
−0.02ns
−0.19***
1
−0.15***
0.19***
0.23***
0.11**
0.08ns
−0.18***
0.25***
0.05ns
−0.14***
0.19***
0.19***
0.09ns
stomata
0.11***
−0.28***
0.08ns
0.27***
1
−0.11***
0.01ns
0.55***
−0.32***
0.26***
−0.04ns
−0.11**
−0.04ns
0.029ns
−0.15***
0.11***
stgth
0.12***
0.19***
−0.21***
−0.24***
−0.23***
1
0.24***
0.25***
−0.1*
−0.63***
0.24***
−0.12**
−0.16***
0.55***
0.49***
0.12***
pltht
0.18***
−0.15***
0.19***
−0.05ns
0.12**
−0.28***
1
0.41***
0.68***
0.18**
0.49***
0.09*
−0.25***
0.54***
0.37***
0.18***
Nnode
0.24***
0.49***
0.029ns
−0.14***
−0.01ns
0.13***
−0.39***
1
−0.17***
0.07ns
0.44***
−0.08*
−0.19***
0.56***
0.33***
0.24***
LA
0.22***
0.37***
0.55***
−0.04ns
0.05ns
−0.29***
0.29***
−0.57***
−0.22***
0.18**
0.09*
−0.01ns
0.01ns
0.31***
0.06ns
0.22***
Nleaf
−0.01ns
0.33***
0.54***
−0.16***
−0.11**
0.25***
0.01ns
0.52***
1
0.58***
0.46***
0.31***
−0.35***
0.26***
0.12**
−0.01ns
Gari wt
0.40***
0.06ns
0.56***
−0.25***
−0.12**
−0.04ns
−0.18***
0.29***
−0.15***
1
0.23***
0.35***
0.40***
−0.09ns
−0.12*
0.40***
Fyld
0.17**
0.12**
0.31***
−0.19***
0.09*
0.24***
0.26***
0.08ns
0.02ns
−0.31***
1
0.43***
−0.09***
0.79***
0.77***
0.17**
HI
0.43***
−0.12*
0.26***
0.01ns
−0.08*
0.49***
−0.63***
−0.32***
−0.54***
0.18***
−0.02ns
1
0.05ns
−0.06ns
0.24***
0.43***
DM
0.26***
0.77***
−0.09ns
−0.35***
−0.01ns
0.44***
0.18**
−0.1*
0.11**
−0.03ns
−0.05ns
−0.01***
1
−0.17***
−0.02ns
0.26***
shtwt
−0.27ns
0.24***
0.79***
0.40***
0.31***
0.09*
0.07ns
0.68***
0.55***
0.23***
0.19***
0.01ns
−0.14***
1
0.78***
−0.27ns
rtno
0.39***
−0.02ns
−0.06ns
−0.09***
0.35***
0.46***
0.18**
−0.17***
0.25***
0.01ns
0.19***
−0.455***
0.03ns
−0.14**
1
0.39***
rtsz
1
0.78***
−0.17***
0.05ns
0.43***
0.23***
0.58***
−0.22***
0.41***
0.24***
−0.11***
−0.15***
−0.049ns
0.19***
−0.34***
1
Table 9.
Correlation coefficient for morphological, physiological, yield, and related traits of 12 cassava genotypes evaluated for 2 years at three locations in Nigeria.
***,**,*: significant at (p ≤ 0.001, 0.01 and 0.05) while ns represents not significant at (p ≤ 0.05), cbbs: cassava bacteria blight mean severity, cassava mosaic disease severity, cads: cassava anthracnose disease severity, chlrphy: chlorophyll, stmata: stomata, stmgrt: stem-girth, ptlht: plant height, LA: leaf area, fyld: fresh yield, Nleaf: Number of leaves, HI: harvest index, DM: Dry Matter, Nroot: Number of roots, rtsz: root size, shtwt: shoot weight, Gari wt: Gari weight.
4. Discussions
As an important environmental limitation, drought has become a rising concern due to its harm to the development and productivity of crop plants [27]. Cassava is a major staple food to resource-limited people in marginal areas because of its ability to survive and produce in such poor land with infrequent rainfall and low fertility [28]. The present study by using 12 cassava genotypes can provide a fundamental basis for the identification of drought-tolerant germplasm resources.
Plants are known to respond to water deficit with some adjustment at morphological, physiological, cellular, and metabolic levels. These responses are, however, dependent upon the duration and severity of stress, the type of genotype/the stage of development, and the organ and cell in question [29]. Reduction in photosynthesis results in the inability of the genotypes to produce tuberous roots compared with conditions with relatively higher moisture levels. Any factor in the plant’s environment that is not the optimum, being either deficient or in excess, will limit plant growth. Moisture stress at 25% F.C. in the screen house led to a reduction in root weight by over 61%, and this is indicative that severe moisture stress that occurs within a period of 8–24 WAP can lead to a very high level of yield loss [30]. Porto [30] also reported that water stress from 4 to 20 WAP led to a reduction of storage root yield by 32–60%. Shoot weight at 25% F.C. was also reduced by 22.4%. Reduction in plant height was 29.3%, leaf chlorophyll content was reduced by 17.7%, leaf number had a reduction of 28.9%, whereas stem girth had a reduction of 15.4%. The implication is that moisture stress could hinder the manifestation of the genetic potentials of cassava. Therefore, a variety that is the best for a trait offers the opportunity (genetic base) for improving such trait through selection or hybridization and further selection. Even with significant differences in the traits that should contribute to root yield, fresh root weight was still not significantly different among genotypes. This also goes along with the result of the screen house experiment by [7]. The presence of the tonoplast sugar transporter (for roots and tubers) to transport the assimilates from the source to the sink (roots) enhances root formation and development. If not, the yield would not be significantly different as shown in this result. Ludewig and Flügge [31] and Cho et al. [32] also report similar observations. There should be continued work on several of the implied and not yet confirmed transport steps within plants, which frequently turn out to be the rate-limiting step to production of valuable compounds in storage sinks.
Plants respond to drought conditions either by increasing or decreasing their root growth.
Relatively higher stomata conductance observed in most genotypes in this study implies that the genotypes would have high photosynthetic potential although not translated to the highest levels of dry matter and root yield. The essence is that this attribute alone might not be a strong contributor to high yield under water-limited conditions. Measurements of leaf diffusive resistance reflected bulk resistance to water loss, combining activities of both the stomata and genotypes. In the dry savannas, genotypes with the ability to optimize water use are desirable due to limited availability of cultivable soil during the dry season [33]. A major component parameter associated with high Water Use Efficiency (WUE) is rapid stomata closure. Generally, stomata resistance of all the genotypes was low in the morning but increased in the afternoon and showed an increase in response to light and vapor pressure deficit (VPD) [34]. The morphophysiological responses to drought stress increases abscisic acid (ABA) concentration, ion transport, and the induction of the associated signaling pathway genes in plant, which in turn increases root resistance by affecting membrane permeability and root tuberization [14]. The rapid reduction in leaf area growth and stomatal closure might be due to cassava’s ability to rapidly synthesize and accumulate ABA at an early phase of a water deficit episode [15].
Plants obtained energy and virtually all their structural materials by photosynthesis and the leaves are the main photosynthetic organ. Jarvis and Morison [35] and Akparobi et al. [36] had similar results. This may indicate that the higher the number of photosynthetic organs, the higher the photosynthetic rate. In this study, correlation between leaf number and root yield was positive and significant. Also, chlorophyll, harvest index, and plant height correlated positively with leaf number.
However, genotypes IITA-TMS-IBA30572 and IITA-TMS-IBA980510 with higher leaf numbers yielded less than IITA-TMS-IBA980581, which had fewer leaves. High leaf number in these genotypes is largely due to their branching habit. Shoot apices and storage roots compete for available carbohydrate in cassava [37, 38]. The high number of branches (level 2) may have caused higher competition for available carbohydrate in IBA30572 than in the other improved genotypes, which probably reduced the root yield in the former genotype despite its high number of leaves. Experiments have shown that production (formation) and growth (elongation) of cassava’s adventitious and lateral roots are suppressed by deficit in soil moisture [7, 39].
The higher leaf area observed in genotype IITA-TMS-IBA011663 did not translate to higher root and dry matter yield. This did not go along with the results of Enyi [37] and [40]), who observed that high yielding cultivars had high leaf area but also validated the findings of (Ludewig and Flügge [31] and Cho et al. [32] that a plant with a good number of stems, better plant height, enough leaf chlorophyll content without the presence of the tonoplast sugar transporter (for roots and tubers) that transports the assimilates from the source to the sink (roots) will not translate to high yields as shown in this result. This might suggest that other parameters of canopy function such as leaf, carbon fixation, and assimilate use and partition could interact significantly in yield formation. Among the improved genotypes, IITA-TMS-IBA011663 possibly partitioned the highest amount of dry matter into leaves, petioles, stems, and fibrous roots and was not the highest in root yield; thus, partitioning of dry matter partially explained the lower yield of IITA-TMS-IBA011663 than in other improved genotypes even though its L.A. was higher.
A better value was observed for stay-green in the improved genotypes IITA-TMS-IBA010040 and IITA-TMS-IBA980581 compared with the value in the local variety TME 1. This implies that the improved genotypes would be expected to be more efficient in supporting photosynthesis, stomata conductance, and carbon fixation. The better stay-green values for IITA-TMS-IBA010040 and IITA-TMS-IBA980581 may be a contributor to their root yield being the best compared with TMEB1, which had the overall worst root yield and the worst stay-green value. However, this finding does not go along with the finding by Oluwafemi et al. [41], which reported that the local variety with high stay-green yielded better than the improved ones.
The lower values that were observed in CMD for new improved genotypes IITA-TMS-IBA980581, IITA-TMS-IBA010040, and IITA-TMS-IBA010034 compared with the checks (local variety TMEB1 and the old-improved varieties IITA-TMS-IBA30572) implied that the new improved genotypes would have healthy clean leaves for better photosynthesis compared with the local variety and the old-improved genotypes. This might be a contributing factor for these genotypes exhibiting better root yields than the old but improved varieties while the local variety had the worst root yield. Cassava mosaic disease was positively correlated with plant height, Gari weight, harvest index, root size, and fresh root yield, whereas CBB was negatively correlated with yield and yield-related traits such as harvest index, plant height, chlorophyll, stomata, root size, and root number.
The significant positive correlations that existed between storage root yields and harvest index indicated that these traits were important in improving root yield. Makame [42] also reported a strong association between storage root yield and root size, while the works of (Radhakrishnan and Gopakumar [43] and Rubaihayo et al.[44] also revealed a strong association between storage root yield and harvest index.
Dry matter in this study was not correlated with storage root yield, thus signifying that dry matter was not an important indicator of storage root yield. Similar observations were also made by Ntawuruhunga [45], Varma and Mathura [46] and Makame [42]. Stomata conductance also showed negative association with storage root yield, indicating this was not an important index of yield. Selections in favor of any of these traits would result in simultaneous decrease in root yield. Negative association between root yield and other traits has also been reported by Makame [42] and Rubaihayo et al. [44].
The high variation that was observed in this study for storage root yield across the different environments indicated that these environments differed greatly as reflected in each location being categorized as a unique agro-ecological zone. The expression of yield, therefore, depends on the genetic factors and on the environmental factors. The genetic factors, which promoted yield, will only be able to express themselves to full capacity when the environmental conditions needed for its expression are found and where this is limiting, yield will be reduced.
Furthermore, effect of storage roots on storage root yield appeared to be positively influenced by root size, root number, and harvest index. [47] made similar assertions and reported that yield increase was mainly due to increase in both numbers of storage roots and individual root weight. Mahungu [48], however, observed that number of tuberous roots contributed more to the final yield than root size. Radhakrishnan and Gopakumar [43] also observed that a high value for harvest index indicated a correspondingly higher yield for storage roots. These three characters can thus be regarded as the most reliable components of yield for selection of high yielding genotypes. A similar assertion was made by [47], who reported that storage root number was more closely correlated with root dry weight than with the individual weight of storage roots and consequently yield increase was mainly due to increase in both numbers of storage roots and individual root weight. Storage root yield, number of storage roots, and root sizes had the greatest contribution to total observed variability, thus suggesting that these characters were the most important of all that were evaluated and improvement on them will lead to improvement in yield and hence, bring progress in crop improvement. The plant breeders and agronomists are, however, interested not only in high yields but also on the ability of genotypes to produce such high yields across diverse environments over several years. Therefore, breeders are interested in identifying genotypes that are stable across environments as well as those that are well adapted and suitable for a specific agro-ecology [49, 50]. Results from the field experiment were able to identify IITA-TMS-IBA980581, IITA-TMS-IBA010034, and IITA-TMS-IBA010040 as having good drought tolerance.
This experiment explained the use of eco-physiological research to improve the genetic base and develop clones that are more adaptable to the drought environments of the tropics. Interdisciplinary research that combines physiology and breeding approach will help in selection of good varieties for release since the performance of a genotype is a function of its adaptability and the availability of conducive environment along with better genetic composition.
Further research is needed and investigation on nutrient transportation, the effect of source and sinks, and their relationship as these affect yield.
Acknowledgments
The authors thank the technical staff of International Institute of Tropical Agriculture (IITA) cassava breeding program that assisted with the fieldwork, screen house evaluation, and laboratory analyses. We also extend our appreciation to the Bill & Melinda Gates Foundation and the United Kingdom’s Foreign, Commonwealth & Development Office (FCDO) for funding this research through the “Next Generation Cassava Breeding Project.”
Contributions
ANA, conceived, designed the study, implemented field trials, performed analyses, and wrote the manuscript, ANA and SM, performed analysis. SM, edited the manuscript, generated, and curated data.
Funding
This research work was generously funded by the Bill & Melinda Gates Foundation and the United Kingdom’s Foreign, Commonwealth & Development Office (FCDO) through the “Next Generation Cassava Breeding project” (award number OPP1048542) managed by Cornell University.
The authors have not declared any conflict of interest.
Data availability statement
All relevant data supporting findings in this study are available on request from the corresponding author.
\n',keywords:"soil moisture, cassava, agro-ecologies, root yield, selection",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/82530.pdf",chapterXML:"https://mts.intechopen.com/source/xml/82530.xml",downloadPdfUrl:"/chapter/pdf-download/82530",previewPdfUrl:"/chapter/pdf-preview/82530",totalDownloads:2,totalViews:0,totalCrossrefCites:0,dateSubmitted:"November 16th 2021",dateReviewed:"May 24th 2022",datePrePublished:"July 5th 2022",datePublished:null,dateFinished:"July 5th 2022",readingETA:"0",abstract:"Cassava is a vital staple crop for many African populations particularly in Nigeria. This study was conducted to determine the effect of soil moisture on the performance of selected 12 cassava genotypes that were evaluated for yield and related traits under three percentages of field capacity (75% – control, 50%, and 25%) in the screen house and field conditions in three agro-ecologies (Ibadan-Derived Savanna, Mokwa-Southern Guinea Savanna, and Zaria-Northern Guinea Savanna) and randomized complete block design was used. Data were collected on plant height, stem girth, number of nodes and leaves, shoot weight, stomata conductant, stay-green, fresh root weight, and dry matter percentage and were analyzed using descriptive statistics and ANOVA. Genotypes differed significantly across and within locations. The higher stress level (25% field capacity – F.C.) resulted in a more significant reduction in vegetative growth than the moderate stress level of 50% F.C.; moisture levels were uniform over time for plant height and stem girth. The response to moisture levels varied widely among genotypes, indicating that they experienced a higher stress condition. Genotypes IITA-TMS-IBA980581, IITA-TMS-IBA010040, and IITA-TMS-IBA010034 were identified with good drought tolerance. Integrating physiological research with breeding efforts will help in the selection of suitable varieties for release.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/82530",risUrl:"/chapter/ris/82530",signatures:"Najimu Adetoro and Sikirou Mouritala",book:{id:"11330",type:"book",title:"Plant Response Mechanisms to Abiotic Stresses",subtitle:null,fullTitle:"Plant Response Mechanisms to Abiotic Stresses",slug:null,publishedDate:null,bookSignature:"Prof. Josphert N. Kimatu",coverURL:"https://cdn.intechopen.com/books/images_new/11330.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-802-8",printIsbn:"978-1-80355-801-1",pdfIsbn:"978-1-80355-803-5",isAvailableForWebshopOrdering:!0,editors:[{id:"224171",title:"Prof.",name:"Josphert N.",middleName:null,surname:"Kimatu",slug:"josphert-n.-kimatu",fullName:"Josphert N. Kimatu"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1 Biochemical and molecular mechanisms known to contribute to water-deficit stress tolerance in cassava plants",level:"2"},{id:"sec_2_2",title:"1.2 Use of crop diversity in plant breeding for drought-tolerance traits",level:"2"},{id:"sec_4",title:"2. Materials and methods",level:"1"},{id:"sec_4_2",title:"2.1 Data collection",level:"2"},{id:"sec_5_2",title:"2.2 Statistical analysis",level:"2"},{id:"sec_7",title:"3. Results",level:"1"},{id:"sec_7_2",title:"3.1 Performance of selected genotypes in different moisture conditions in the screen house and field environments",level:"2"},{id:"sec_8_2",title:"3.2 Overall fields disease means scores, yield, and yield-related traits of 12 cassava genotypes evaluated for 2 years at three locations in Nigeria",level:"2"},{id:"sec_9_2",title:"3.3 Morphological and physiological field performance of 12 cassava genotypes evaluated at three locations in Nigeria for 2 years",level:"2"},{id:"sec_10_2",title:"3.4 Location means recorded by 12 genotypes evaluated for 2 years at three locations in Nigeria",level:"2"},{id:"sec_11_2",title:"3.5 The correlation coefficients for traits measured for 2 years at three locations in Nigeria",level:"2"},{id:"sec_13",title:"4. Discussions",level:"1"},{id:"sec_14",title:"Acknowledgments",level:"1"},{id:"sec_14",title:"Contributions",level:"1"},{id:"sec_15",title:"Funding",level:"1"},{id:"sec_16",title:"Declaration of interest",level:"1"},{id:"sec_17",title:"Data availability statement",level:"1"}],chapterReferences:[{id:"B1",body:'FAO. The State of Food Security and Nutrition in the World: Safeguarding against Economic Slowdowns and Downturns. Italy: FAO; 2019'},{id:"B2",body:'Montagnac JA, Davis CR, Tanumihardjo SA. Nutritional value of cassava for use as a staple food and recent advances for improvement. Comprehensive Reviews in Food Science and Food Safety. 2009;8:181-194'},{id:"B3",body:'Asante-Pok A. Analysis of Incentives and Disincentives for Cassava in Nigeria. Rome: FAO; 2013'},{id:"B4",body:'FAO. Save and Grow Cassava: A Guide to Sustainable Production Intensification. 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Effect of shoot number and time of planting on growth, development and yield of Cassaya (Manihot Esculenta Crantz). Journal of Horticultural Sciences. 1972;47:457-466'},{id:"B38",body:'Lian T, Cock JH. Cassava plant forms and their associated morphophysiological characters. MARDI Research Bulletin. 1979;7:55-69'},{id:"B39",body:'Bergantin RV, Yamauchi A, Pardales JR Jr, Bolatete-Al DM. Screening cassava genotypes for resistance to water deficit during crop establishment. Philippines Journal of Crop Science. 2004;29:29-39'},{id:"B40",body:'Lahai M, George J, Ekanayake I. Cassava (Manihot esculenta Crantz) growth indices, root yield and its components in upland and inland valley ecologies of Sierra Leone. Journal of Agronomy and Crop Science. 1999;182:239-248'},{id:"B41",body:'Oluwafemi Z, Omonona B, Adepoju A, Sowunmi F. Cassava productivity growth in Nigeria. Asian Journal of Research and Agriculture. 2019:1-9'},{id:"B42",body:'Makame M. Genetic variation, stability of performance of cassava clones and their responses to intercropping with sweet potato in Tanzania [Ph.D. Thesis]. Ibadan, Nigeria: University of Ibadan; 1995; 248 p'},{id:"B43",body:'Radhakrishnan V, Gopakumar K. Correlation between yield and its components in tapioca. Indian Journal of Agricultural Sciences. 1984;54:975-978'},{id:"B44",body:'Rubaihayo P, Whyte J, Dixon A, Osiru D. Inter-relationships among traits and path analysis for yield components of cassava: A search for storage root yield indicators. African Crop Science Journal. 2001;9:599-606'},{id:"B45",body:'Ntawuruhunga P. Assessment of dry matter determination and its accumulation in cassava (Manihot esculenta Crantz) [Ph.D. thesis]. University of Ibadan; 1992'},{id:"B46",body:'Varma S, Mathura R. Genetic variability and inter-relation in cassava (Manihot esculenta Crantz) under rainfed conditions of Tripura. Journal of Root Crops. 1993;19:77-80'},{id:"B47",body:'Kasele I, Ekanayake IJ, Dixon AGO. Preliminary investigations on photosynthetic capacity and nutrient uptake of colloid cassava (Manihot esculenta Crantz). In: Akoroda MO, Ekanayake IJ, editors. Root Crop and Poverty Alleviation Proceedings of Sixth Symposium of ISTRC-AB. Lilongwe, Malawi; 1998'},{id:"B48",body:'Mahungu N. Relationship among selected agronomic characters and their effects on tuberous root yield of cassava (Manihot esculenta Cratz) [Ph.D. Thesis]. Nigeria: University of Ibadan; 1983; p. 193'},{id:"B49",body:'Acuña TB, Lafitte H, Wade LJ. Genotype× environment interactions for grain yield of upland rice backcross lines in diverse hydrological environments. Field Crops Research. 2008;108:117-125'},{id:"B50",body:'Nassir AL, Adewusi KM. Genotype x environment analysis of root traits of upland rice (Oryza sativa L.) in a drought prone tropical rainfed ecology. Tropical Agriculture. 2015;92:16-26'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Najimu Adetoro",address:"n.adetoro@cgiar.org",affiliation:'
International Institute of Tropical Agriculture (IITA), Nigeria
International Institute of Tropical Agriculture (IITA), Democratic Republic of the Congo
International Institute of Tropical Agriculture (IITA), Democratic Republic of the Congo
International Institute of Tropical Agriculture, Democratic Republic of the Congo
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IntechOpen’s Academic Editors and Authors have received funding for their work through many well-known funders, including: the European Commission, Bill and Melinda Gates Foundation, Wellcome Trust, Chinese Academy of Sciences, Natural Science Foundation of China (NSFC), CGIAR Consortium of International Agricultural Research Centers, National Institute of Health (NIH), National Science Foundation (NSF), National Aeronautics and Space Administration (NASA), National Institute of Standards and Technology (NIST), German Research Foundation (DFG), Research Councils United Kingdom (RCUK), Oswaldo Cruz Foundation, Austrian Science Fund (FWF), Foundation for Science and Technology (FCT), Australian Research Council (ARC).
Open Access publication costs can often be designated directly in the grants or in specific budgets allocated for that purpose. Many of the most important funding organisations encourage, and even request, that the projects they fund are made available at no cost to the wider public. IntechOpen strives to maintain excellent relationships with these funders and ensures compliance with mandates.
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In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
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\\n\\t
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If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
\\n\\n
Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at funders@intechopen.com
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Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.
Open Access publication costs can often be designated directly in the grants or in specific budgets allocated for that purpose. Many of the most important funding organisations encourage, and even request, that the projects they fund are made available at no cost to the wider public. IntechOpen strives to maintain excellent relationships with these funders and ensures compliance with mandates.
\n\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
\n\n
\n\t
Does your institution already have a budget for covering Open Access publication costs?
\n\t
Does your grant list Open Access publication fees as legitimate direct/indirect costs?
\n
\n\n
If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
\n\n
Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at funders@intechopen.com
\n\n
Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.
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Yet, little is known about the HRQoL of Turkish and Polish migrants and German natives.",book:{id:"5598",slug:"people-s-movements-in-the-21st-century-risks-challenges-and-benefits",title:"People's Movements in the 21st Century",fullTitle:"People's Movements in the 21st Century - Risks, Challenges and Benefits"},signatures:"Johanna Buchcik, Joachim Westenhöfer, Mick Fleming and Colin R.\nMartin",authors:[{id:"192396",title:"Dr.",name:"Johanna",middleName:null,surname:"Buchcik",slug:"johanna-buchcik",fullName:"Johanna Buchcik"},{id:"193514",title:"Prof.",name:"Joachim",middleName:null,surname:"Westenhöfer",slug:"joachim-westenhofer",fullName:"Joachim Westenhöfer"},{id:"193515",title:"Prof.",name:"Mick",middleName:null,surname:"Fleming",slug:"mick-fleming",fullName:"Mick Fleming"},{id:"193516",title:"Prof.",name:"Colin R.",middleName:null,surname:"Martin",slug:"colin-r.-martin",fullName:"Colin R. Martin"}]},{id:"53700",doi:"10.5772/67013",title:"Migration and Health from a Public Health Perspective",slug:"migration-and-health-from-a-public-health-perspective",totalDownloads:2389,totalCrossrefCites:3,totalDimensionsCites:8,abstract:"One of the main dimensions related to migration is that of health; this correlation is dynamic by nature and complex. Health is strongly related to the social determinants of health (job, income, education, and housing). When not properly supported by appropriate inter-sectoral policies, migration can expose the most vulnerable socioeconomic groups to significant problems. The protection of the health of migrants is an important investment of the public health because it promises benefits to both migrant population and natives. An essential aspect is to properly analyze the health needs of ethnic minorities. Both quantitative and qualitative research is necessary, and the involvement of the target communities is important. Another important aspect is the education and training of social and health workers involved in the care of migrants (with a multidisciplinary teamwork and “transcultural” approach), and the organization of services that can effectively be used. Finally, it is also essential to carry out an evaluation of health outcomes of the migrant population and the impact of adopted health policies. Protecting the health of ethnic minorities is both a challenge for governments and a test of the quality of their health systems.",book:{id:"5598",slug:"people-s-movements-in-the-21st-century-risks-challenges-and-benefits",title:"People's Movements in the 21st Century",fullTitle:"People's Movements in the 21st Century - Risks, Challenges and Benefits"},signatures:"Maurizio Marceca",authors:[{id:"192600",title:"Prof.",name:"Maurizio",middleName:null,surname:"Marceca",slug:"maurizio-marceca",fullName:"Maurizio Marceca"}]},{id:"53657",doi:"10.5772/66827",title:"Asians as Model Minorities: A Myth or Reality among Scientists and Engineers in Academia",slug:"asians-as-model-minorities-a-myth-or-reality-among-scientists-and-engineers-in-academia",totalDownloads:1523,totalCrossrefCites:4,totalDimensionsCites:4,abstract:"Asians from China, India, South Korea, and Taiwan constitute the largest non‐White group in academic science and engineering (S&E). Most of the studies in relation to race/ethnicity combine Asians into one category whether they are immigrants (foreign born) or US citizens. Research has suggested that job satisfaction differs with the type of citizenship status held by faculty members. However, what studies fail to notice is that Asian faculty members who are either born in the United States or are naturalized might experience very different levels of attitudes and satisfaction toward their job when compared with Asian faculty members who are foreign born and on temporary visa status, impacting retention. Do institutions recognize the differences between these two groups, or are Asian faculty members considered a “model minority” group and “problem‐free?” This is the question that this study aims to examine. Given the growing competition in S&E globally, matters pertaining to faculty members’ satisfaction, retention, and persistence will take a front seat among policy makers and university administrators. Data for this study come from the National Science Foundation’s Survey of Doctorate Recipients (SDR).",book:{id:"5598",slug:"people-s-movements-in-the-21st-century-risks-challenges-and-benefits",title:"People's Movements in the 21st Century",fullTitle:"People's Movements in the 21st Century - Risks, Challenges and Benefits"},signatures:"Meghna Sabharwal",authors:[{id:"192631",title:"Associate Prof.",name:"Meghna",middleName:null,surname:"Sabharwal",slug:"meghna-sabharwal",fullName:"Meghna Sabharwal"}]},{id:"53671",doi:"10.5772/67004",title:"The New Actors of International Migration: A Comparative Analysis of Foreign Students’ Experiences in a Medium-Sized City in Turkey",slug:"the-new-actors-of-international-migration-a-comparative-analysis-of-foreign-students-experiences-in-",totalDownloads:1258,totalCrossrefCites:0,totalDimensionsCites:4,abstract:"International or foreign student migration is one of the topics that started to become a hot topic in many different countries because of various aspects. Lately, Turkey, especially in terms of higher education, can be seen as a country that sends students to study abroad but also attracts foreign students. With regard to Adnan Menderes University which is located in a medium‐sized city (Aydın), the main focus of this study is based on experiences of foreign students regarding prejudice, discrimination and racism. In addition to this, the topics such as to what extent are foreign students having difficulty to adapt to the life in Turkey, to what extent are the students satisfied with their lives in the country, in what way could foreign students who currently study in Turkey help to increase the foreign student population or sustain the student migration were examined. In this context, six different categories, such as students from Africa, Asia, South Caucasia, Middle East, Europe/Balkans and Europe/Other, were created. Surveys were applied to participants of the study group in line with a quantitative research scope.",book:{id:"5598",slug:"people-s-movements-in-the-21st-century-risks-challenges-and-benefits",title:"People's Movements in the 21st Century",fullTitle:"People's Movements in the 21st Century - Risks, Challenges and Benefits"},signatures:"Serdar Ünal",authors:[{id:"192402",title:"Dr.",name:"Serdar",middleName:null,surname:"Ünal",slug:"serdar-unal",fullName:"Serdar Ünal"}]},{id:"53486",doi:"10.5772/66824",title:"Immigration and Food Insecurity: The Canadian Experience—A Literature Review",slug:"immigration-and-food-insecurity-the-canadian-experience-a-literature-review",totalDownloads:2598,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Canada is a popular destination for immigrants and integration of newcomers is an important strategy for its demographic growth and economic development. Food insecurity disproportionately affects newcomers in Canada; unfortunately, they occupy the lower end of the socio‐economic spectrum and thus adding to the burden of socio‐cultural challenges they are already facing. The high level of food insecurity contributes to poor diet quality and the rise in overweight and other chronic health conditions and therefore to the loss of healthy immigrant status. Indeed, statistical evidence, mainly of the overall Canadian population, demonstrates that individuals living in food‐insecure households have higher rates of self‐reported poor health and chronic health conditions. Therefore, understanding and properly addressing the factors associated with food insecurity among Canadian immigrants is crucial for an adequate integration of immigrants. This chapter suggests that an adequate and appropriate understanding of food security for Canadian immigrant populations requires consideration of a cultural perspective in addition to the traditional individual, household and community levels and the development of measurement tools to capture this cultural dimension. It is proposed the concept of cultural food insecurity encompasses the four usual dimensions (availability, accessibility, utilization, and stability) and a newly proposed fifth cultural dimension. Future research should aim at validating the relevance of this cultural perspective as a fifth pillar for food security and developing measurement tools to assess it.",book:{id:"5598",slug:"people-s-movements-in-the-21st-century-risks-challenges-and-benefits",title:"People's Movements in the 21st Century",fullTitle:"People's Movements in the 21st Century - Risks, Challenges and Benefits"},signatures:"Diana Tarraf, Dia Sanou and Isabelle Giroux",authors:[{id:"192842",title:"M.Sc.",name:"Diana",middleName:null,surname:"Tarraf",slug:"diana-tarraf",fullName:"Diana Tarraf"},{id:"193533",title:"Dr.",name:"Dia",middleName:null,surname:"Sanou",slug:"dia-sanou",fullName:"Dia Sanou"},{id:"193603",title:"Dr.",name:"Isabelle",middleName:null,surname:"Giroux",slug:"isabelle-giroux",fullName:"Isabelle Giroux"}]}],mostDownloadedChaptersLast30Days:[{id:"53486",title:"Immigration and Food Insecurity: The Canadian Experience—A Literature Review",slug:"immigration-and-food-insecurity-the-canadian-experience-a-literature-review",totalDownloads:2594,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Canada is a popular destination for immigrants and integration of newcomers is an important strategy for its demographic growth and economic development. Food insecurity disproportionately affects newcomers in Canada; unfortunately, they occupy the lower end of the socio‐economic spectrum and thus adding to the burden of socio‐cultural challenges they are already facing. The high level of food insecurity contributes to poor diet quality and the rise in overweight and other chronic health conditions and therefore to the loss of healthy immigrant status. Indeed, statistical evidence, mainly of the overall Canadian population, demonstrates that individuals living in food‐insecure households have higher rates of self‐reported poor health and chronic health conditions. Therefore, understanding and properly addressing the factors associated with food insecurity among Canadian immigrants is crucial for an adequate integration of immigrants. This chapter suggests that an adequate and appropriate understanding of food security for Canadian immigrant populations requires consideration of a cultural perspective in addition to the traditional individual, household and community levels and the development of measurement tools to capture this cultural dimension. It is proposed the concept of cultural food insecurity encompasses the four usual dimensions (availability, accessibility, utilization, and stability) and a newly proposed fifth cultural dimension. Future research should aim at validating the relevance of this cultural perspective as a fifth pillar for food security and developing measurement tools to assess it.",book:{id:"5598",slug:"people-s-movements-in-the-21st-century-risks-challenges-and-benefits",title:"People's Movements in the 21st Century",fullTitle:"People's Movements in the 21st Century - Risks, Challenges and Benefits"},signatures:"Diana Tarraf, Dia Sanou and Isabelle Giroux",authors:[{id:"192842",title:"M.Sc.",name:"Diana",middleName:null,surname:"Tarraf",slug:"diana-tarraf",fullName:"Diana Tarraf"},{id:"193533",title:"Dr.",name:"Dia",middleName:null,surname:"Sanou",slug:"dia-sanou",fullName:"Dia Sanou"},{id:"193603",title:"Dr.",name:"Isabelle",middleName:null,surname:"Giroux",slug:"isabelle-giroux",fullName:"Isabelle Giroux"}]},{id:"53470",title:"Sociocultural Models of Second-Language Learning of Young Immigrants in Canada",slug:"sociocultural-models-of-second-language-learning-of-young-immigrants-in-canada",totalDownloads:1632,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The most significant challenge for the minority immigrant is learning a new language. They arrive in a new culture and community hoping to master English quickly in order to achieve their academic and career goals. However, many immigrants have mentioned general barriers resulting from being unable to communicate with peers outside their cultural and linguistic group. Recent research has identified several cognitive variables such as vocabulary, reading aloud, and grammatical judgment related to second-language learning in immigrants; however, little attention was given to sociocultural factors such as acculturation, motivation, and cultural learning because learning a language is a necessary aspect of being socialized into a particular culture. This chapter reviews research of sociocultural models in relation to second-language learning of immigrant youth in Canada. We address this paradigm for research by incorporating both acculturation and sociolinguistic approaches, as well as more traditional cognitive-linguistic approaches, to models of second-language learning in immigrants.",book:{id:"5598",slug:"people-s-movements-in-the-21st-century-risks-challenges-and-benefits",title:"People's Movements in the 21st Century",fullTitle:"People's Movements in the 21st Century - Risks, Challenges and Benefits"},signatures:"Fanli Jia, Alexandra Gottardo and Aline Ferreira",authors:[{id:"192435",title:"Dr.",name:"Fanli",middleName:null,surname:"Jia",slug:"fanli-jia",fullName:"Fanli Jia"},{id:"194425",title:"Dr.",name:"Alexandra",middleName:null,surname:"Gottardo",slug:"alexandra-gottardo",fullName:"Alexandra Gottardo"},{id:"194656",title:"Dr.",name:"Aline",middleName:null,surname:"Ferreira",slug:"aline-ferreira",fullName:"Aline Ferreira"}]},{id:"53700",title:"Migration and Health from a Public Health Perspective",slug:"migration-and-health-from-a-public-health-perspective",totalDownloads:2388,totalCrossrefCites:3,totalDimensionsCites:8,abstract:"One of the main dimensions related to migration is that of health; this correlation is dynamic by nature and complex. Health is strongly related to the social determinants of health (job, income, education, and housing). When not properly supported by appropriate inter-sectoral policies, migration can expose the most vulnerable socioeconomic groups to significant problems. The protection of the health of migrants is an important investment of the public health because it promises benefits to both migrant population and natives. An essential aspect is to properly analyze the health needs of ethnic minorities. Both quantitative and qualitative research is necessary, and the involvement of the target communities is important. Another important aspect is the education and training of social and health workers involved in the care of migrants (with a multidisciplinary teamwork and “transcultural” approach), and the organization of services that can effectively be used. Finally, it is also essential to carry out an evaluation of health outcomes of the migrant population and the impact of adopted health policies. Protecting the health of ethnic minorities is both a challenge for governments and a test of the quality of their health systems.",book:{id:"5598",slug:"people-s-movements-in-the-21st-century-risks-challenges-and-benefits",title:"People's Movements in the 21st Century",fullTitle:"People's Movements in the 21st Century - Risks, Challenges and Benefits"},signatures:"Maurizio Marceca",authors:[{id:"192600",title:"Prof.",name:"Maurizio",middleName:null,surname:"Marceca",slug:"maurizio-marceca",fullName:"Maurizio Marceca"}]},{id:"53859",title:"Introductory Chapter: People's Movements in the 21st Century",slug:"introductory-chapter-people-s-movements-in-the-21st-century",totalDownloads:1490,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"5598",slug:"people-s-movements-in-the-21st-century-risks-challenges-and-benefits",title:"People's Movements in the 21st Century",fullTitle:"People's Movements in the 21st Century - Risks, Challenges and Benefits"},signatures:"Ingrid Muenstermann",authors:[{id:"77112",title:"Dr.",name:"Ingrid",middleName:null,surname:"Muenstermann",slug:"ingrid-muenstermann",fullName:"Ingrid Muenstermann"}]},{id:"53701",title:"The Immigrant Experience in V.S. Naipaul's The Enigma of Arrival and Z. Smith's White Teeth: An Exploration of Homi Bhabha's Postcolonial Theory",slug:"the-immigrant-experience-in-v-s-naipaul-s-the-enigma-of-arrival-and-z-smith-s-white-teeth-an-explora",totalDownloads:2088,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"V.S. Naipaul and Z. Smith, prominent postcolonial authors, reflect the condition of the immigrants suffering from cultural shock, hybridity, fragmentation and mimicry in the postcolonial Western societies in their novels, The Enigma of Arrival and White Teeth. The former portrays the desperate condition of an author doing his best to create his work in the post‐war West, in London and New York, trying to overcome his hybridity and adaptation problems due to his cultural background, and the latter sheds light on the cultural distress of two families from Bangladesh, immigrating to London, by stressing the conflicts between the Westerners and the Easterners and between the first and the second generations of immigrants. Thus, these two novels highlight the immigrant experience illustrating the impact of power relations between the former colonized and the former colonizer upon their relationship in the postcolonial era. In this study, the problems of immigrants in the post‐war West in these novels will be analysed in the light of Homi Bhabha's postcolonial theory, which puts forward such concepts as hybridity, mimicry, ambivalence, cultural differentiation and otherness. In this regard, Bhabha's theory will be adapted into these novels to identify cultural problems of immigrants in these works.",book:{id:"5598",slug:"people-s-movements-in-the-21st-century-risks-challenges-and-benefits",title:"People's Movements in the 21st Century",fullTitle:"People's Movements in the 21st Century - Risks, Challenges and Benefits"},signatures:"Berna Köseoğlu",authors:[{id:"148215",title:"Dr.",name:"Berna",middleName:null,surname:"Köseoğlu",slug:"berna-koseoglu",fullName:"Berna Köseoğlu"}]}],onlineFirstChaptersFilter:{topicId:"1325",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:320,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:133,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:17,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"July 5th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). In addition to a number of research articles, he has written two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.",institutionString:null,institution:{name:"Stellenbosch University",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",isOpenForSubmission:!0,annualVolume:11418,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,annualVolume:11419,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,annualVolume:11420,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. 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His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,annualVolume:11421,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. 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In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). 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He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}}]}},subseries:{item:{id:"19",type:"subseries",title:"Animal Science",keywords:"Animal Science, Animal Biology, Wildlife Species, Domesticated Animals",scope:"The Animal Science topic welcomes research on captive and wildlife species, including domesticated animals. 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A dynamic career research platform which is based on the thematic areas of comparative vertebrate physiology, stress endocrinology, reproductive endocrinology, animal health and welfare, and conservation biology. \nEdward has supervised 40 research students and published over 60 peer reviewed research.",institutionString:null,institution:{name:"University of Queensland",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null,series:{id:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517"},editorialBoard:[{id:"258334",title:"Dr.",name:"Carlos Eduardo",middleName:null,surname:"Fonseca-Alves",slug:"carlos-eduardo-fonseca-alves",fullName:"Carlos Eduardo Fonseca-Alves",profilePictureURL:"https://mts.intechopen.com/storage/users/258334/images/system/258334.jpg",institutionString:null,institution:{name:"Universidade Paulista",institutionURL:null,country:{name:"Brazil"}}},{id:"191123",title:"Dr.",name:"Juan José",middleName:null,surname:"Valdez-Alarcón",slug:"juan-jose-valdez-alarcon",fullName:"Juan José Valdez-Alarcón",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSBfcQAG/Profile_Picture_1631354558068",institutionString:"Universidad Michoacana de San Nicolás de Hidalgo",institution:{name:"Universidad Michoacana de San Nicolás de Hidalgo",institutionURL:null,country:{name:"Mexico"}}},{id:"161556",title:"Dr.",name:"Maria Dos Anjos",middleName:null,surname:"Pires",slug:"maria-dos-anjos-pires",fullName:"Maria Dos Anjos Pires",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS8q2QAC/Profile_Picture_1633432838418",institutionString:null,institution:{name:"University of Trás-os-Montes and Alto Douro",institutionURL:null,country:{name:"Portugal"}}},{id:"209839",title:"Dr.",name:"Marina",middleName:null,surname:"Spinu",slug:"marina-spinu",fullName:"Marina Spinu",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRLXpQAO/Profile_Picture_1630044895475",institutionString:null,institution:{name:"University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca",institutionURL:null,country:{name:"Romania"}}},{id:"92185",title:"Dr.",name:"Sara",middleName:null,surname:"Savic",slug:"sara-savic",fullName:"Sara Savic",profilePictureURL:"https://mts.intechopen.com/storage/users/92185/images/system/92185.jfif",institutionString:'Scientific Veterinary Institute "Novi Sad"',institution:{name:'Scientific Veterinary Institute "Novi Sad"',institutionURL:null,country:{name:"Serbia"}}}]},onlineFirstChapters:{paginationCount:14,paginationItems:[{id:"82457",title:"Canine Hearing Management",doi:"10.5772/intechopen.105515",signatures:"Peter M. 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