Impact of biochar on Surface area (SA) and porosity of amended soils
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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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\r\n\tDepigmentation disorders may be congenital or acquired. Congenital diseases of depigmentation include oculocutaneous albinism, piebaldism, Waardenburg syndrome, tuberous sclerosis, nevus depigmentosus and cutaneous mosaicism. Acquired diseases of depigmentation include vitiligo, hypomelanosis secondary to cutaneous inflammation, infectious hypomelanosis, chemical or pharmacologic hypomelanosis, hypomelanosis from physical agents, and miscellaneous. Vitiligo is a very common acquired depigmentation disorder where melanocytes that produce melanin pigment of the skin are destroyed. It can occur systematically and affect whole body or locally/segmentally affecting parts of the body. For the treatment of vitiligo, various medical, surgical and photo therapies are in use, and several emerging new treatments are under investigation.
\r\n\r\n\tThese disorders can profoundly affect patient's quality of life. To improve facial blemishes, many people resort to laser treatments, chemical peels and cosmetics, thus markets for depigmenting lasers and cosmeceuticals are also increasing rapidly. This book aims to cover topics in diseases of depigmentation and therapeutic depigmentation.
",isbn:"978-1-83969-012-9",printIsbn:"978-1-83969-011-2",pdfIsbn:"978-1-83969-013-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"3e1efdb1fc8c403c402da09b242496c6",bookSignature:"Dr. Tae-Heung Kim",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9589.jpg",keywords:"Congenital Depigmentation, Albinism, Piebaldism, Dyschromatosis Hereditaria, Vitiligo, Pathogenesis, Phototherapy, Excimer Laser, Excimer Light, Surgical Treatments, Postinflammatory Hypopigmentation, Pityriasis Alba",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 9th 2020",dateEndSecondStepPublish:"November 23rd 2020",dateEndThirdStepPublish:"January 22nd 2021",dateEndFourthStepPublish:"April 12th 2021",dateEndFifthStepPublish:"June 11th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Kim graduated from and acquired a doctoral degree (Ph.D.) at Seoul National University College of Medicine. He is an active member of many international and domestic societies and a former President of the Korean Society for Vitiligo.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"121353",title:"Dr.",name:"Tae-Heung",middleName:null,surname:"Kim",slug:"tae-heung-kim",fullName:"Tae-Heung Kim",profilePictureURL:"https://mts.intechopen.com/storage/users/121353/images/system/121353.png",biography:"Dr. Tae-Heung Kim graduated from and acquired a doctoral degree (PhD) at Seoul National University College of Medicine. He completed an internship and dermatology residency at Seoul National University Hospital.\r\nHe moved to the Department of Dermatology, Gyeongsang National University, and was then promoted to Professor and Chairman of Dermatology.\r\nIn 1996, he did a research sabbatical for two years at the Department of Immunology, University of Texas MD Anderson Cancer Center.\r\nIn 2003, he started private practice as Director of the White-Line Skin Clinic and Research Center, Changwon, Kyungnam.\r\nHe is an active member of many international and domestic societies, and was the President of the Korean Society for Vitiligo (2016–2018).",institutionString:"White-Line Skin Clinic & Research Center",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"184402",firstName:"Romina",lastName:"Rovan",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/184402/images/4747_n.jpg",email:"romina.r@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"49186",title:"Biochar Technology for Sustainable Organic Farming",doi:"10.5772/61440",slug:"biochar-technology-for-sustainable-organic-farming",body:'Throughout the world, intensive agriculture has often led to decline in soil physical, chemical and biological properties, leading to soil degradation. This decline in soil quality may be due to erosion and mining of nutrients and organic matter, hence preventing the soil from performing its functions such as regulating water flow, storing and cycling of nutrients, filtering, and transformation of organic and inorganic materials and sustaining biological productivity. However, considerably large amount of wastes such as crop residues, animal manure, etc. are being produced from many agricultural production systems. This organic waste may represent a considerable problem as well as new challenges and opportunities depending on how they are handled, which may determine whether there will be increase or decrease in biomass production, organic matter supply and decomposition rate.
In addressing the issue of decline in soil fertility, [1] reported that intentional and unintentional deposition of nutrient-rich materials on farmlands have in many cases led to an increase in soil fertility status. However, fresh residue materials have been reported to decompose until almost all carbon is lost [2]. This practice may not be sustainable when compared to the ever-growing human population per time. Thus, conversion of biomass to biochar could alter the transformation dynamics with respect to carbon sequestration. Soil carbon sequestration offers a large and long-term carbon sink to agricultural soils. Biochar is one of the sources of soil carbon sink, which could be obtained by subjecting biomass to pyrolysis. Pyrolysis is a process of combusting organic materials (biomass) under limited oxygen level [3].
Biochar as a soil amendment has become an important topic in soil science in the past few years, and the effects of biochar on agro-ecosystems are being studied by many researchers [4]. The conversion of biomass to bio-char as a carbon sink has been proposed before [5], but was not explicitly linked to an application to soil. As a soil amendment, biochar can greatly influence various soil properties and processes [6]. In fact, biochar may occur as a component of soil organic matter where slash-and-burn agriculture is widely practiced [7]. Many of the organic residues from agriculture, forestry and other production systems can be used to produce biochar and applied to agricultural soil both to sequester carbon and to improve the production potential of crops. This renewed focus in agriculture can be said to have started as a result of the discovery of the Terra Preta de Indo soils (Figure 1) located in the Amazon River Basin. From the assumptions surrounding the formation of the Terra Preta soils, agricultural scientists have come to believe that soil properties could be amended by applying biochar as an amendment [3]. Hence, biochar, the carbon-enriched, fine-grained product of biomass combusted under conditions of limited oxygen, is currently being widely studied for its effects as a soil amendment.
[8] defined biochar as a carbon-enriched, fine-grained and porous by-product of slow pyrolysis when organic material (feedstock) is thermally decomposed at low–moderate temperatures during long heating times under limited supply of oxygen. Feedstock may include wood materials, tree bark, crop residues, chicken litter, dairy manure or sewage sludge. Biochar is chemically and biologically more stable than the original fresh form from which it is produced due to its molecular configuration [9], making it more difficult to breakdown. This means that, in some cases, it can remain stable in soils for hundreds to thousands of years [10].
Pictorial view of Latosol (left) and Terra Preta (right) soil horizon.
Biochars are characterized by certain morphological and chemical properties which are borne from the physico-chemical alteration of the original feedstock as a result of pyrolytic process. Characteristically, these properties of biochar differ since they are controlled by factors such as type of organic material from which they are made, pyrolysis conditions (i.e. final pyrolysis temperature or peak temperature, rate of heat application – slow or fast pyrolysis), rate and duration of charring [11,12,13]. The impact of biochar as an amendment depends on its properties. Key properties of biochar are the adsorptive properties that potentially alter soil’s surface area, pore size distribution, bulk density, water-holding capacity and penetration resistance. Some physical properties of biochar determined by variations in feedstock type and pyrolysis condition are discussed below.
Large surface area amendment property of biochar contributes to the adsorptive properties of soil and potentially improves pore size distribution, bulk density and consequently leading to an increase in the soil available water needed for crop growth and development. In addition, a strong direct relationship exists between a biochar’s surface area and the pore volume as measured using N2 adsorption and Braunauer-Emmett-Teller (BET) modelling [14,15]. [15] reported that the surface area could also be measured by using other compounds such as CO2 on carbonaceous materials at the micrometer scale. [16] stated that understanding and determination of the relative abundance and stability of pores of different sizes are keys to soil ecosystem functioning. Important among these functions are aeration, hydrology and provision of habitat for microbes while the finer pores could be involved with molecular adsorption and transport [17].
Differences in production conditions, especially final combustion temperature, would result to variation in surface area of biochars even when they are produced from the same parent biomass. [16] stated that the relationship between the peak combustion temperature and surface morphological parameters (i.e. surface area, pore diameter and volume) of the resulting biochar is highly complex. [18] stated that there may be either no simple relationship between surface area and peak temperature, or surface area may increase with increase in peak temperature up to a certain threshold and then decrease. Due to variations in reports on surface area and peak temperature, [16] reported that the mechanisms responsible for increases in surface area with an increase in peak temperature or heating rate are not well understood. However, [11] reported that surface area increases with an increase in peak temperature of biochar production.
The adsorptive nature of biochar is related to its surface area. The adsorptive capability of biochar is determined by its surface chemical properties and porous nature. It is an important physical property due to its influence in the uptake and binding effect of materials from their surroundings [16]. [19] reported that biochar may adsorb poly aromatic compounds, poly aromatic and poly aliphatic hydrocarbons, other toxic chemicals, metals and elements or pollutants in soils, sediments, aerosols and water bodies.
This important physical property makes biochar a more sustainable soil amendment relative to its original fresh biomass for agricultural purpose. The evidence of high amounts of black carbon in the Terra Preta soils over a time suggests a high recalcitrant nature of biochar. However, degradation of at least some components (volatile matter or labile organic matter) of the biochar may occur [20]. On the other hand, [16] noted that the difference in sub-soil characteristics due to variations in microbial activity and oxygen content may affect biochar oxidation and aging. Biochar can move into sub-soil over time [21] to enrich the zone. Hence, other factors associated with its physical stability in soil include its mobility into deeper soil profile [16]. The aggregate stability of biochar-amended soil may also determine the susceptibility of biochars to microbial processes in subsoil. Mukherjee and Lal [16] explained that these factors not only enhance the stability of soil organic matter in the deeper profile but also improve availability of water and nutrients to crops and decrease erosion risks.
Biochar has the potential capacity to restore a degraded soil when added to the soil. Biochar mineralizes gradually over a long period of time when applied to the soil. Nutrients from biochar are released gradually to improve the physical, chemical and biological conditions of the soil. [12] reported that the impact of biochar as an amendment is a function of its properties such as large surface area and presence of micropores. These are key properties because they contribute to the adsorptive properties of soils and potentially alter soil physical and hydrological properties.
Figure 2 illustrates the interaction between biochar and soil. The application of biochar to the soil will alter the physical and chemical properties of the soil. [22] stated that the net effect of biochar on the soil physical properties will depend on its interaction the physico-chemical characteristics of the soil, the weather conditions prevalent at the particular site and the management of its application. Biochar application can reduce the bulk density of the different soils [23]. This could bring about improvement in soil structure or aggregation, and aeration enhancement, thus improving soil porosity. [17] reported that the higher the total porosity (micro- and macropores) the higher is soil physical quality. This is because micropores are involved in molecular adsorption and transport of water and nutrients while macropores affect aeration and drainage. Several studies have reported that as low as 0.5% (g g−1) biochar application rate was sufficient to improve water-holding capacity and water retention [24,25]. Hence, this can be said to be good water-holding capacity amendment for sandy soils which are highly porous due to the preponderance of macropores.
Schematic representation of interactions between biochar and soil [16].
A key determinant of soil functions and processes is its physical properties, precisely and most importantly, its texture. Hence, the addition of biochar in soils with different textures should affect the soil hydraulic properties differently due to the fact that there is a correlation between soil texture and soil hydraulic properties. The impacts of biochar as a soil amendment on some soil physical and hydrological properties are briefly discussed below.
Table 1 depicts a summary of results of biochar application on surface area. Soil surface area is an intrinsic property of soil determined by the sizes of its particles. The surface area of soils is an important physical characteristic which plays a vital role in water- and nutrient-holding capacities, aeration and microbial activities [26]; hence, it can be said to be partly controlling the essential functions of soil fertility. However, the effectiveness of the surface area of a soil depends on its size – the larger the surface area, the greater the soil’s water- and nutrient-holding capacities. This is particularly true for fine-textured soils. Thus, [16] reported that agronomic productivity improvement of biochar-amended soils may be linked to the higher surface area of the biochar–soil mixtures. [17,27,28] explained that the high surface area of biochar provides the space for formation of bonds and complexes with cations and anions with metals and elements of soil on its surface, which improves the nutrient retention capacity of soil. [28] reported that biochar incorporation can enhance specific surface area up to 4.8 times that of adjacent soils. [29] also reported increases in specific surface area of an amended clayey soil from 130 to 150 m2 g–1 when biochar derived from mixed hardwoods was applied at rates of 0 to 20 g kg–1 in a long-term soil column incubation study.
Table 1 shows a summary of results of biochar application on soil porosity. This is the ratio of the pore volume to the total volume of a representative sample of a porous medium. This factor is said to be associated with surface area. The total porosity or pore size distribution of biochar is a factor that can play an important role in the alteration of the properties of biochar-amended soils. Biochars are usually characterized by the preponderance of micropores, which may alter the pore size distribution of coarse texture soil when added. [24] reported that significant increases in mesoporosity occurred at the expense of macropores in waste-derived biochar-amended soil compared to the control. [24] further intensified that the higher the rate of biochar application the greater its effect on porosity. Hence, biochar could be a good replacement for tillage practices which causes short-term increase in porosity, but long-term decrease in aggregation and ultimately lowering soil porosity.
\n\t\t\t\tSoil type\n\t\t\t | \n\t\t\t\n\t\t\t\tBiochar type\n\t\t\t | \n\t\t\t\n\t\t\t\tStudy type (scale)\n\t\t\t | \n\t\t\t\n\t\t\t\tRate of biochar application `\n\t\t\t | \n\t\t\t\n\t\t\t\tSA\n\t\t\t | \n\t\t\t\n\t\t\t\tPorosity\n\t\t\t | \n\t\t\t\n\t\t\t\tReference\n\t\t\t | \n\t\t||
\n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | (g g–1) | \n\t\t\t(m2 g–1) | \n\t\t\t(%) | \n\t\t\t\n\t\t | |
Residue sand | \n\t\t\tMunicipal green waste, 450οC | \n\t\t\tLaboratory | \n\t\t\t0 | \n\t\t\t- | \n\t\t\t0.46 | \n\t\t\t[24] | \n\t\t||
\n\t\t\t | 2.6 | \n\t\t\t- | \n\t\t\t0.48 | \n\t\t\t\n\t\t | ||||
\n\t\t\t | \n\t\t\t | \n\t\t\t | 5.2 | \n\t\t\t- | \n\t\t\t0.51 | \n\t\t\t\n\t\t | ||
Clarion fine loamy | \n\t\t\tMixed hardwoods (Oak: Quercus spp., Hickory: Carya spp.), 500οC | \n\t\t\tLaboratory | \n\t\t\t0 | \n\t\t\t130 | \n\t\t\t- | \n\t\t\t[29] | \n\t\t||
\n\t\t\t | 0.5 | \n\t\t\t133 | \n\t\t\t- | \n\t\t\t\n\t\t | ||||
\n\t\t\t | 1.0 | \n\t\t\t138 | \n\t\t\t- | \n\t\t\t\n\t\t | ||||
\n\t\t\t | 2.0 | \n\t\t\t153 | \n\t\t\t- | \n\t\t\t\n\t\t | ||||
Sandy soil | \n\t\t\tJarrah woods (Eucalyptus marginata), 600οC | \n\t\t\tGreenhouse | \n\t\t\t0 | \n\t\t\t1.3 | \n\t\t\t56.1 | \n\t\t\t[61] | \n\t\t||
\n\t\t\t | 0.45 | \n\t\t\t2.7 | \n\t\t\t57.6 | \n\t\t\t\n\t\t | ||||
\n\t\t\t | 2.27 | \n\t\t\t8.4 | \n\t\t\t62.1 | \n\t\t\t\n\t\t | ||||
Silt loam | \n\t\t\tBirch (Betula pendula), 400οC | \n\t\t\tField | \n\t\t\t0 | \n\t\t\t- | \n\t\t\t50.9 | \n\t\t\t[62] | \n\t\t||
\n\t\t\t | 1.2 | \n\t\t\t- | \n\t\t\t52.8 | \n\t\t\t\n\t\t |
Impact of biochar on Surface area (SA) and porosity of amended soils
Table 2 shows the results of biochar application on soil bulk density. Bulk density, which is defined as the mass of soil per its unit volume, has been known to have a negative correlation with surface area. [30] stated that well-structured soils (fine texture) are characterized by low bulk density values between 1.0 and 1.3 g cm–3 while poorly structured (coarse texture) soils are known to have high bulk density values between 1.6 and 1.8 g cm–3. Hence, reports from both field and laboratory studies have shown bulk densities to have contrasting results to surface areas of biochar-amended soils. [29], [24] and [23] reported that application of biochar can decrease the bulk density of soils. [29] showed in a soil column incubation study that biochar-amended soil columns had significantly lower bulk density than no-biochar controls. [16] reported that biochar-amended column had a lower rate of compaction compared to the control or manure-amended soil columns when all the columns were subjected to compaction by gravity and periodical leaching events. They further stated that the decrease in bulk density of biochar-amended soil could be one of the indicators of the improvement of soil structure or aggregation and aeration, and could be soil-specific.
\n\t\t\t\tSoil types\n\t\t\t | \n\t\t\t\n\t\t\t\tBiochar type\n\t\t\t | \n\t\t\t\n\t\t\t\tStudy type (scale)\n\t\t\t | \n\t\t\t\n\t\t\t\tRate of biochar application\n\t\t\t | \n\t\t\t\n\t\t\t\tBulk density\n\t\t\t | \n\t\t\t\n\t\t\t\tReference\n\t\t\t | \n\t\t
\n\t\t\t | \n\t\t\t | \n\t\t\t | % (g g–1) | \n\t\t\tg cm–3\n\t\t\t | \n\t\t\t\n\t\t |
Norfolk loamy sand: E | \n\t\t\tPecan (Carya illinoinensis) shells, 700οC | \n\t\t\tLaboratory | \n\t\t\t0 | \n\t\t\t1.52 | \n\t\t\t[32] | \n\t\t
\n\t\t\t | \n\t\t\t | \n\t\t\t | 2.1 | \n\t\t\t1.451, 1.522\n\t\t\t | \n\t\t\t\n\t\t |
Norfolk loamy sand: E and Bt | \n\t\t\t\n\t\t\t | \n\t\t\t | 0 | \n\t\t\t1.34 | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 2.1 | \n\t\t\t1.361, 1.342\n\t\t\t | \n\t\t\t\n\t\t |
Hydroagric stagnic anthrosol | \n\t\t\tWheat (Triticum spp.) straw, 350–550οC | \n\t\t\tField | \n\t\t\t0 | \n\t\t\t0.99, 0.943\n\t\t\t | \n\t\t\t[63] | \n\t\t
\n\t\t\t | \n\t\t\t | \n\t\t\t | 1.1 | \n\t\t\t0.96, 0.913\n\t\t\t | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 2.2 | \n\t\t\t0.91, 0.863\n\t\t\t | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 4.4 | \n\t\t\t0.89, 0.883\n\t\t\t | \n\t\t\t\n\t\t |
Residue sand | \n\t\t\tMunicipal green waste , 450οC | \n\t\t\tLaboratory | \n\t\t\t0 | \n\t\t\t1.65 | \n\t\t\t[24] | \n\t\t
\n\t\t\t | \n\t\t\t | \n\t\t\t | 2.6 | \n\t\t\t1.55 | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 5.2 | \n\t\t\t1.44 | \n\t\t\t\n\t\t |
Clarion fine loamy | \n\t\t\tMixed hardwoods, 500οC | \n\t\t\tLaboratory | \n\t\t\t0 | \n\t\t\t1.21, 1.344\n\t\t\t | \n\t\t\t[29] | \n\t\t
\n\t\t\t | \n\t\t\t | \n\t\t\t | 0.5 | \n\t\t\t1.10, 1.244\n\t\t\t | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 1.0 | \n\t\t\t1.08, 1.244\n\t\t\t | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 2.0 | \n\t\t\t1.08, 1.244\n\t\t\t | \n\t\t\t\n\t\t |
Soil bulk density as affected by biochar application
Source: [16].\n\t\t\t\t\t\t\t\t\t\t1 measured after 44 days; 2 measured after 94 days; 3 measured after 1 year; 4 measured after 15 months.
Results of studies showing biochar effect on soil aggregation are illustrated in Table 3. Studies have shown biochar to respond positively to aggregation. Though [16] reported that data on aggregate stability and penetration resistance of biochar-amended soils are scarce, a few studies generally showed that low-temperature (220οC) hydrochar made from spent brewer’s grains (a residue from beer brewing) responded positively to aggregation of Albic Luvisol by significantly increasing water-stable aggregates as compared to the control treatment. [31] have reported that the formation of complexes of biochar with minerals, as the result of interactions between oxidized carboxylic acid groups at the surface of biochar particles, should be responsible for the improved soil aggregate stability (Figure 2). As a result, soil aggregates and pore size distribution can be improved by adding organic matter from biodegradation and thus improving soil hydraulic properties. However, other authors have reported contrasting results. For instance, [32] reported that with or without mixing Bt and E horizons with pecan shell (Carya illinoinensis), biochar-amended soil decreased aggregation compared to the control, while [33] reported mixing of biochar from pecan with switchgrass increased aggregation, but the effect was however significantly lower when the soil was treated only with biochar without mixing with switchgrass. From this trend of results, [16] concluded that a positive effect on soil aggregate stability would require the presence of a substrate (i.e switchgrass) along with biochar as an amendment.
Studies on the effect of biochar amendment on soil penetration resistance are illustrated in Table 3. Penetration resistance measures the capacity of a soil in its confined state to resist penetration by a rigid object [34]. It is affected by moisture content. Thus, it affects the potential for root growth and development. Ehlers et al. [35] found root growth to be inversely related to penetration resistance. Results from literatures have shown that the effect of biochar application on soil penetration resistance is dependent on time of application. Busscher et al. [32] reported that mixing Norfolk loamy sand E and E and Bt layers with pecan shell biochar produced at a temperature of 700οC increased penetration resistance measured after 44 days of application. Penetration resistance was, however, reduced when measured after 96 days of application. Thus, soil compaction may not be alleviated by biochar addition over short period of time, but may be altered in the long run due to changes in properties as a result of aging of biochar.
\n\t\t\t\tSoil types\n\t\t\t | \n\t\t\t\n\t\t\t\tBiochar type\n\t\t\t | \n\t\t\t\n\t\t\t\tStudy type (Scale)\n\t\t\t | \n\t\t\t\n\t\t\t\tRate of biochar application\n\t\t\t | \n\t\t\t\n\t\t\t\tAggregation\n\t\t\t | \n\t\t\t\n\t\t\t\tPenetration resistance\n\t\t\t | \n\t\t\t\n\t\t\t\tReference\n\t\t\t | \n\t\t
\n\t\t\t | \n\t\t\t | \n\t\t\t | % (g g–1) | \n\t\t\t% | \n\t\t\tMPa | \n\t\t\t\n\t\t |
Norfolk loamy sand: E | \n\t\t\tPecan shells, 700οC | \n\t\t\tLaboratory | \n\t\t\t0 | \n\t\t\t14.3 | \n\t\t\t1.191, 0.802\n\t\t\t | \n\t\t\t[32] | \n\t\t
\n\t\t\t | \n\t\t\t | \n\t\t\t | 2.1 | \n\t\t\t12.9 | \n\t\t\t1.271, 0.882\n\t\t\t | \n\t\t\t\n\t\t |
Norfolk loamy sand: E and Bt | \n\t\t\t\n\t\t\t | \n\t\t\t | 0 | \n\t\t\t27.3 | \n\t\t\t0.711, 0.762\n\t\t\t | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 2.1 | \n\t\t\t20.9 | \n\t\t\t0.881, 0.942\n\t\t\t | \n\t\t\t\n\t\t |
Norfolk loamy sand: Ap | \n\t\t\tPecan shells, 700οC | \n\t\t\tLaboratory | \n\t\t\t0 | \n\t\t\t9.95, 13.0*\n\t\t\t | \n\t\t\t1.041, 1.12\n\t\t\t | \n\t\t\t[33] | \n\t\t
\n\t\t\t | \n\t\t\t | \n\t\t\t | 0.5 | \n\t\t\t9.53, 12.7*\n\t\t\t | \n\t\t\t0.961, 1.152\n\t\t\t | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 1.0 | \n\t\t\t10.7, 12.3*\n\t\t\t | \n\t\t\t1.031, 1.022\n\t\t\t | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 2.0 | \n\t\t\t9.23, 11.8*\n\t\t\t | \n\t\t\t0.821, 0.872\n\t\t\t | \n\t\t\t\n\t\t |
Albic Luvisol | \n\t\t\tHydrochar, 220οC | \n\t\t\tLaboratory | \n\t\t\t0 | \n\t\t\t49.8 | \n\t\t\t- | \n\t\t\t[64] | \n\t\t
\n\t\t\t | \n\t\t\t | \n\t\t\t | 5 | \n\t\t\t69.0 | \n\t\t\t- | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 10 | \n\t\t\t65.1 | \n\t\t\t- | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | Greenhouse | \n\t\t\t0 | \n\t\t\t10.3 | \n\t\t\t- | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 5 | \n\t\t\t20.8 | \n\t\t\t- | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 10 | \n\t\t\t33.8 | \n\t\t\t- | \n\t\t\t\n\t\t |
Soil aggregation and penetration resistance as affected by biochar application
Several authors have reported positive response of soil hydrological properties to biochar amendment. This may be due to the fact that soil hydrological properties such as infiltration rate, moisture content, hydraulic conductivity, water-holding capacity and water retention are invariably related to soil surface area, bulk density, porosity and aggregate stability [16]. In other words, an alteration in these soil physical properties as caused by biochar application would lead to a change in soil hydrological properties.
Table 4 shows the results of biochar application effect on water-holding capacity. The amount of water in a soil is a function of its ability to hold and retain water for plant use against the influence of gravity. Fine-textured soils would have higher moisture content at the same tension as soils with coarse particles. This is because the ability of a soil to retain water is a function of the micropores in the soil, which is usually lower in coarse-textured soils. Hence, moisture required by plants to upset the evapotranspirational demand of the atmosphere may be limiting, especially in coarse-textured soils. Thus, application of biochar can increase water-storage ability of coarse-textured soils. Several studies have reported alterations in water-holding capacity and water retention in soils amended with biochar. [33] and [36] reported that 0.5% (g g–1) biochar application rate was sufficient to improve water-holding capacity. Application of biochar produced from black locust (Robinia pseudoacacia) was reported to increase the available water capacity by 97%, saturation water content by 56%, but reduced hydraulic conductivity [25]. This can also influence soil aeration and temperature to a very large extent. [29] reported that results from a long-term column study indicated that biochar-amended Clarion soil retained up to 15% more water, with 13% and 10% more water retention at –100 KPa and –500 KPa soil matric potential, respectively, compared to control (unamended soils). [37] showed that coal-derived humic acid substances can increase water retention, available water capacity and aggregate stability of inherently degraded soils. [38] reported that biochar application increased the available water capacity in sandy soil, with no effect on a loamy soil, and decreased moisture content in a clayey soil. [16] suggested that such response may be due to the hydrophobic nature of the charcoal that caused alterations in soil pore size distribution. [38], therefore, advised that because the soil moisture retention may only be improved in coarse-textured soils, a careful choice of biochar/soil combination needs to be taken into consideration.
\n\t\t\t\tSoil types\n\t\t\t | \n\t\t\t\n\t\t\t\tBiochar type\n\t\t\t | \n\t\t\t\n\t\t\t\tStudy type (Scale)\n\t\t\t | \n\t\t\t\n\t\t\t\tRate of biochar application\n\t\t\t | \n\t\t\t\n\t\t\t\tWater holding capacity\n\t\t\t | \n\t\t\t\n\t\t\t\tReference\n\t\t\t | \n\t\t
\n\t\t\t | \n\t\t\t | \n\t\t\t | % (g g–1) | \n\t\t\t(g cm–3) | \n\t\t\t\n\t\t |
Residue sand | \n\t\t\tMunicipal green waste, 450οC | \n\t\t\tLaboratory | \n\t\t\t0 | \n\t\t\t0.11 | \n\t\t\t[24] | \n\t\t
\n\t\t\t | \n\t\t\t | \n\t\t\t | 2.6 | \n\t\t\t0.16 | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 5.2 | \n\t\t\t0.20 | \n\t\t\t\n\t\t |
Norfolk loamy sand: Ap | \n\t\t\tPecan shells, 700οC | \n\t\t\tLaboratory | \n\t\t\t0 | \n\t\t\t0.64 | \n\t\t\t[33] | \n\t\t
\n\t\t\t | \n\t\t\t | \n\t\t\t | 0.5 | \n\t\t\t0.59 | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 1.0 | \n\t\t\t0.60 | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 2.0 | \n\t\t\t0.66 | \n\t\t\t\n\t\t |
Sandy loam | \n\t\t\tPonderosa pine (Pinus ponderosa), 450οC | \n\t\t\tLaboratory | \n\t\t\t0 | \n\t\t\t11.9 | \n\t\t\t[36] | \n\t\t
\n\t\t\t | \n\t\t\t | \n\t\t\t | 0.5 | \n\t\t\t12.4 | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 1.0 | \n\t\t\t13.0 | \n\t\t\t\n\t\t |
\n\t\t\t | \n\t\t\t | \n\t\t\t | 5.0 | \n\t\t\t18.8 | \n\t\t\t\n\t\t |
Silt loam | \n\t\t\tBirch, 400οC | \n\t\t\tField | \n\t\t\t0 | \n\t\t\t0.49 | \n\t\t\t[62] | \n\t\t
\n\t\t\t | \n\t\t\t | \n\t\t\t | 1.2 | \n\t\t\t0.54 | \n\t\t\t\n\t\t |
Soil water holding capacity as affected by biochar application
Most studies of biochar as a soil amendment have focused majorly on soil nutrient status, taking into consideration cation exchange capacity, nutrient content, pH, the carbon sequestration potential of the amended soil, and vegetative growth and yield of crops. Biochar has the potential to improve soil CEC due to the fact that it is often characterized by high CEC values, due to its negative surface charges and its high specific surface area as was reported for biochar produced from crop residues [39].
Furthermore, the immediate beneficial effect of biochar application on crop productivity in tropical soils may result from increase in availability of nitrogen, phosphorus, potassium, calcium, copper and zinc as reported for soils amended with secondary forest biochar [40]. Also, poultry litter biochar may result in strong increase in soil extractable phosphorus [41] when incorporated into the soil. In evaluating the effect of different biochars on soil chemical properties, [42] reported that biochar produced from poultry manure had higher electrical conductivity, nitrogen, phosphorus and pH values than that of garden waste. However, this may be due to their effects in reducing leaching and fixation of nutrients as moderate biochar additions are not a direct supplier of plant nutrients in the long-term.
Biochar application can directly or indirectly affect SOC dynamics. Indirectly, biochar could affect net primary production and, thus, the amount of biomass that may remain in agro-ecosystems. This would result to alteration in soil carbon inputs. [8] stated that higher below-ground net primary production and increased root-derived carbon inputs after biochar application may particularly result in an increase in SOC.
Directly, biochar can inhibit degradation process, and as a result increase the mean residence time (MRT) of SOC (i.e. the mean time that a SOC-carbon atom spends in soil). As a direct consequence, biochar application would enhance SOC stabilization processes and contribute to SOC sequestration. The MRT of biochar-carbon is thought by some to be in the range of millennia [43]. However, information on biochar longevity in soil is meagre and varies between biochars and sites. For example, the MRTs of biochar in field experiments ranged from about 8 years for biochar produced by burning of forest trees during slash-and-burn agricultural practices [44] to 3,600 years for biochar produced from prunings of old mango (Mangifera indica L.) trees [45]. Also, biochar longevity in soil may be affected by differences in climatic conditions. For example, chemical and/or biological mineralization of natural chars produced from wood during bushfires was slower under Mediterranean climate when compared to temperate climates in Australia [46].
Biochar can be said to be acidic or alkaline in nature depending on the temperature of the materials used during pyrolysis. [47] explained that the acid functional group concentration in biochars produced from the biomass of rice, valley oak (Quercus lobata\n\t\t\t\t\tNe´ e), etc decreased with increasing peak pyrolysis temperature as more fused aromatic ring structures were produced and more volatile matter was lost. The effectiveness of both types will depend on the pH of the soil to be amended. [48] stated that the alkaline biochars produced at higher pyrolysis temperature are more effective in supporting increases in biomass by improved growth conditions than acidic biochars presumably through increases in soil alkalinity. [49] stated that the moderation in aluminium toxicity may be the reason why biochar application has positive effects on productivity in tropical and irrigated systems on highly weathered and acid soils with low-activity clays. This is because the reduction of aluminium and iron concentrations in the soil solution will enhance the availability of previously bound phosphorus to plants, and plant roots would be able to explore even acid soils to absorb nutrients and water more effectively.
Studies have shown higher microbial biomass but yet lower microbial activity in biochar-amended soil than the neighbouring soils [50]. However, most studies have focused on biochar interaction with mycorrhizal fungi [50]. Specifically, biochar has been reported to have symbiotic relationship with the mycorrhizal system. According to [51], the four mechanisms by which biochar could improve mycorrhizal abundance (40%) and functioning are listed as follows:
Alteration of soil physico-chemical properties,
Indirect effects on mycorrhizae through effects on other soil microbes,
Plant-fungus signalling interference, and
Detoxification of allelochemicals on biochar.
[52] noted 50% to 72% increase in soil biological nitrogen fixation through biochar application. [53] have hypothesized both bacteria and fungi to be better protected from grazers or competitors by exploring pore habitats in biochars. This is because biochar provides microbial habitat and refugia for microbes where they are also protected from unfavourable conditions.
The summary of experiments assessing the impact of biochar addition on crop yield is showed in Table 5. From the agricultural perspective, the summary of the effect of biochar in regulating soil hydrological, physical and chemical properties results to improved soil productivity and consequently increased crop yield. However, the effect of biochar on soil health as well as crop productivity can be influenced by the forms (dust, fine particles, coarse grain) and the methods of application (surface application, top dressing, drilling) of biochar to soil. [54] clearly explained that even small quantities of biochar added to seed coatings may in some cases be sufficient for a beneficial effect.
[40] reported increasing crop yields with increasing biochar applications of up to 140 t carbon ha–1 on highly weathered soils in the humid tropics. Also, [55] found that the biomass growth of beans rose with biochar applications up to 60 t carbon ha–1. Furthermore, scientists have reported that application of biochar on soil has significant effect on net primary crop production, grain yield and dry matter production [56,57,58,59].
\n\t\t\t\tAuthor\n\t\t\t | \n\t\t\t\n\t\t\t\tStudy outline\n\t\t\t | \n\t\t\t\n\t\t\t\tResults summary\n\t\t\t | \n\t\t
[65] | \n\t\t\tCowpea on xanthic ferralsol | \n\t\t\tChar at 67 t/ha increased biomass by 150% | \n\t\t
\n\t\t\t | \n\t\t\t | Char at 135 t/ha increased biomass by 200% | \n\t\t
[40] | \n\t\t\tSoil fertility and nutrient retention. Cowpea was planted in pots and rice crops in lysimeters, Brazil | \n\t\t\tBiochar additions significantly increased biomass production by 38% to 45% (no yield reported) | \n\t\t
[66] | \n\t\t\tComparison of maize yields between disused charcoal production sites and adjacent fields, Ghana | \n\t\t\tGrain and biomass yield was 91% and 44% higher on charcoal site than control | \n\t\t
[67] | \n\t\t\tMaize, cowpea and peanut trial in area of low soil fertility | \n\t\t\t\n\t\t\t\tAcacia bark charcoal plus fertilizer increased maize and peanut yields (but not cowpea) | \n\t\t
[42] | \n\t\t\tPot trial on radish yield in heavy soil using commercial green waste biochar (three rates) with and without nitrogen | \n\t\t\tBiochar at 100 t/ha increased yield ×3; linear increase 10 to 50 t/ha, but no effect without added nitrogen | \n\t\t
Biochar, as an amendment on soil physical, chemical and biological properties, depends on environmental conditions, dynamic properties of soils, biochar properties which are a function of the organic materials and conditions used for biochar production and the rate and method of application.
Notable soil physical properties found to be enhanced by biochar include soil surface area, bulk density, porosity, aggregate stability, penetration resistance and moisture content. Also, soil pH, organic carbon and cation exchange capacity were enhanced in biochar-amended soils. Biologically, mycorrhizal abundance, biological nitrogen fixation, microbial biomass and microbial habitats were improved in biochar-amended soils compared to unamended soils.
Modification of soil physical, chemical and biological properties by biochar application resulted to improved plant nutrient retention, acquisition and availability, leading to improved biomass growth, dry matter production and crop yields.
Explainable artificial intelligence (xAI) is one of the research topics that has been intriguing in recent years. Today, even if we are at the beginning of understanding this type of models, the studies that show interesting results about this issue are getting more and more intensive. In the near future, it is predicted that there will be years when the interpretability of artificial intelligence and deep meta-learning models is frequently explored [1]. It is thought to be a solution to overcome constraints in classical deep learning methods.
In classical artificial intelligence approaches, we frequently encounter deep learning methods available today. Currently, in classical deep learning methods, input data and target (class) information can be trained with high performance and tested with new data input [2]. These deep learning methods can yield highly effective results according to the data set size, data set quality, the methods used in feature extraction, the hyper parameter set used in deep learning models, the activation functions, and the optimization algorithms [3]. Many layers in a deep network allow it to recognize things at different levels of abstraction. For example, in a structure designed to recognize dogs, the lower layers recognize simple things such as outlines or color; the upper layers recognize more complex things like fur or eyes, and the upper layers define them all as a dog. Presumably speaking, the same approach can be applied to other inputs that lead a machine to teach itself. For example, it can be easily applied to the sounds that make up the words in the speech, the letters and words that form the sentences in the text, or the steering movements required to drive.
However, there are important shortcomings that current deep learning models are currently inadequate [4]. For deep learning, huge data sets are needed to train on, and these data sets must be inclusive/unbiased, and of good quality [5]. In addition, traditional deep learning requires a lot of time to train models for satisfying their purpose with an admissible amount of accuracy and relevancy [6]. Although deep learning is autonomous, it is highly susceptible to errors. Assume that an algorithm is trained with data sets small enough to not be inclusive [4]. The models trained by this way cause to irrelevant responses (biased predictions coming from a biased training set) being displayed to users [7]. One of the most important problems in artificial learning models is transparency and interpretability [8]. These artificial neural network-based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable [9]. In other words, when you receive an output data against the input data, the deep learning model cannot provide the information for which reason the output is generated. The user cannot fully grasp the internal functions of these models and cannot find answers to question why and how the answers the models produce [10]. This situation creates difficulties in the application areas of these models in many aspects. For example, you stopped a taxi and got on it. The driver is such a driver that when he takes you to your destination, he turns right, turns left, and tries to get you on a strange route than you expect, but when you ask why he did so, he cannot give you a satisfactory answer. Would you be nervous? If there is no problem for you, you can ride an autonomous vehicle without a driver. As another example, when you go to the doctor, the doctor you send your complaint asks for tests and when you have those tests and send it to the doctor, the doctor tells you what your illness is. Even though he says his treatment, he does not give explanatory information about the cause of your illness. In this case, questions remain about what caused the disease and you would not be satisfied with the doctor. This is an important open point in artificial neural networks and deep learning models.
The explainable artificial intelligence (xAI) approach can be considered as an area at the intersection of several areas. One of these areas is the end user explanation section that includes social sciences. This area provides artificial intelligence to gain cognitive abilities. Another area is the human machine interface, where it can demonstrate the ability to explain; because explainable artificial intelligence needs a very high-level interaction with the user. And finally, deep learning models are an important part of an explicable artificial intelligence approach (Figure 1).
Explainable artificial intelligence (xAI) [8].
In this new approach, it is aimed to provide the user with the ability to explain the output data produced as well as being trained at high performance with the input data and target (class) information and tested with the new data input as in the classical machine learning models. This will create a new generation artificial intelligence approach that can establish a cause and effect relationship between input and output. It will also be the mechanism of monitoring the reliability of artificial intelligence from the user point of view. While a classic deep learning model can answer “what” or “who” questions, learning models in explainable artificial intelligence approaches can also answer “why,” “how,” “where,” and “when” questions [10] (Figure 2).
How can explainable artificial intelligence (xAI) be reliable [11]?
Explainability and accuracy are two separate domains. In general, models that are advantageous in terms of accuracy and performance are not very successful in terms of explainability. Likewise, methods with high explainability are also disadvantageous in terms of accuracy. When methods such as classical deep learning models, artificial neural networks support vector machines are utilized, they do not give reasons why, and how their outputs created in terms of explainability. On the other hand, they are very successful in accuracy and performance. Rule-based structures, decision trees, regression algorithms, and graphical methods are good explainability but not advantageous in terms of performance and accuracy. At this point, explanatory artificial intelligence (xAI), which is targeted to be at the highest level of both explainability and accuracy and performance, reveals its importance at this point (Figure 3).
Machine learning models with respect to accuracy-explainability domain [12].
There is a transformation of machine learning that has been going on since the 1950s, sometimes faster and sometimes slower. The most studied and remarkable area in the recent past is artificial learning, which aims to model the live decision system, behavior, and responses. Successful results in the field of artificial learning led to the rapid increase of AI applications. Further studies promise to be autonomous systems capable of self-perception, learning, decision-making, and action [13].
Especially after the 1990s, although deep learning concept and foundations go back to the past, the accompanying recurrent neural networks, convolutional neural networks, deep reinforcement learning, and adversarial generative networks have achieved remarkable successes. Although successful results are obtained, these systems are insufficient in terms of explaining the decisions and actions to human users and there are limits.
The U.S. Department of Defense (DoD) explains that it is facing the challenges posed by autonomous and symbiotic systems, which are becoming smarter with each passing day. Explaining artificial intelligence or especially explanatory machine learning is important in terms of being a preview that users will encounter machines with human-like artificial intelligence in the future [14, 15]. Explained artificial intelligence is one of the Defense Advanced Research Projects Agency (DARPA) programs aimed at the development of a new generation of artificial intelligence systems, where they understand the context and environment in which machines operate and build descriptive models that enable them to characterize the real world phenomenon over time. For this purpose, DARPA recently issued a call letter for the Explainable Artificial Intelligence (XAI)—Explanatory Artificial Intelligence project [15]. Within the scope of the project, it is aimed to develop a system of machine learning techniques that focus on machine learning and human-machine interaction, and produce explanatory models that will enable end users to understand, trust, and manage emerging artificial intelligence systems. According to the researchers from DARPA, the striking successes in machine learning have led to a huge explosion in new AI capabilities that enable the production of autonomous systems that perceive, learn, decide, and act on their own. Although these systems provide tremendous benefits, their effectiveness is limited due to the inability to explain machine decisions and actions to human users.
The Explanatory Artificial Intelligence project aims to develop the machine learning and computer-human interaction tools to ensure that the end user, who depends on decisions, recommendations, or actions produced by the artificial intelligence system, understands the reason behind the system’s decisions [1]. For example, an intelligence analyst who gets recommendations from big data analytics algorithms may need to understand why the algorithm advises to examine a particular activity further. Similarly, the operator, who tests a newly developed autonomous system, has to understand how he makes his own decisions to determine how the system will use it in future tasks.
The xAI tools will provide end users with explanations of individual decisions, which will enable them to understand the strengths and weaknesses of the system in general, give an idea of how the system will behave in the future, and perhaps teach how to correct the system\'s mistakes. The XAI project addresses three research and development challenges: how to build more models, how to design an explanation interface, and how to understand psychological requirements for effective explanations [2].
For the first problem, the xAI project aims to develop machine learning techniques to be able to manufacture explanatory models. To solve the second challenge, the program envisions integrating state-of-the-art human-machine interaction techniques with new principles, strategies, and techniques to produce effective explanations. To solve the third problem, the xAI project plans to summarize, disseminate, and apply existing psychological theory explanations. There are two technical areas in the program: the first is to develop an explanatory learning system with an explanatory model and an explanation interface; and the second technical area covers psychological theories of explanation [8].
In 2016, a self-driving car was launched on quiet roads in Monmouth County, New Jersey. This experimental tool developed by researchers at chip maker Nvidia did not look different from other autonomous cars; however, Google was different from what Tesla or General Motors introduced and showed the rising power of artificial intelligence. The car had not even followed a single instruction provided by an engineer or a programmer. Instead, it relied entirely on an algorithm that allowed him to learn to drive by watching a person driving [3]. It was an impressive success to have a car self-driving in this way. But it was also somewhat upsetting as it was not entirely clear how the car made its own decisions. The information from the vehicle’s sensors went directly to a huge artificial neural network that processes the data and then delivers the commands needed to operate the steering wheel, brakes, and other structures. The results seem to match the reactions you can expect from a human driver. But what if one day something unexpected happens; hits a tree or stops at the green light? According to the current situation, it may be difficult to find the cause. The system is so complex that even the engineers who designed it can find it difficult to pinpoint the cause of any action. Moreover, you cannot ask this; there is no obvious way to design such a system that can always explain why it does what it does. The mysterious mind of this vehicle points to a vague-looking issue of artificial intelligence. Artificial intelligence technology, which is located at the base of the car and known as deep learning, has proven to be very strong in problem-solving in recent years, and this technology has been widely applied in works such as image content estimation, voice recognition, and language translation. Now the same methods can be used to diagnose lethal diseases, make million-dollar business decisions, etc. to change all industries.
Currently, the mathematical models are used to help determine who will be on parole, who will be approved to borrow money, and who will be hired. If you can access these mathematical models, it is possible to understand their reasoning. But banks, the military, employers, and others are now turning their attention to more complex machine learning approaches. These approaches can make automated decision-making completely incomprehensible. The most common of these approaches represents deep learning, a fundamentally different way of programming computers. Whether it is an investment decision or a medical decision, or a military decision, you do not want to rely solely on a “black box” method [1]. There is already a debate that it is a fundamental legal right to question a system of artificial intelligence about how it arrived at its conclusions. Starting in the summer of 2018, the European Union may require companies to provide users with an explanation of the decisions made by automated systems. This may be impossible even for systems that look comparatively simple on the surface, such as applications and Websites that use deep learning to offer advertising or song suggestions. Computers performing these services have programmed themselves and have done so in ways we cannot understand. Even the engineers who build these applications cannot fully explain their behavior.
As technology advances, we can go beyond some thresholds where using artificial intelligence in recent times requires a leap of faith. The mankind, of course, are not always able to fully explain our thought processes; but we find a variety of methods to intuitively trust people and measure them. Will this be possible for machines that think and make decisions differently than a person does? We have never built machines that operate in ways that their manufacturers do not understand. How long can we hope to communicate and deal with intelligent machines that can be unpredictable or incomprehensible? These questions take a journey toward new technology research on artificial intelligence algorithms, from Google to Apple and many other places between them, including a conversation with one of the greatest thinkers of our time.
You cannot see how the deep neural network works just by looking inside. The reasoning of a network is embedded in the behavior of thousands of nerves, which are stacked and tied to tens or even hundreds of layers, mixed together. Each of the nerves in the first layer receives an input, such as the voltage of a pixel in an image, and then performs a calculation before sending a new signal as an output. This output is sent to the next layer in a complex network, and this process continues until a general output is produced. There is also a process known as back propagation that modifies the calculations of individual nerves so that a network learns to produce a desired output. Because deep learning is inherently a dark black box by nature, artificial learning models designed with millions of artificial nerve cells with hundreds of layers like traditional deep learning models are not infallible [1]. Their reliability is questioned when simple pixel changes can be seriously misleaded by causing significant deviations in the weight values in all layers of the neural network, especially in an example such as a one-pixel attack [16]. So, it becomes inevitable to ask the question of how it can succeed or fail. With the success of this type of advanced applications, its complexity also increases and its understanding/clarity becomes difficult.
It is aimed to have the ability to explain the reasons of new artificial learning systems, identify their strengths and weaknesses, and understand how they will behave in the future. For an ideal artificial intelligence system, the best accuracy and best performance, as well as the best explainability and the best interpretability are required within the cause-effect relationship. The strategy developed to achieve this goal is to develop new or modified artificial learning techniques that will produce more explicable models. These models are aimed to be combined with state-of-the-art human-computer interactive interface techniques that can be translated into understandable and useful explanation dialogs for the end user (Figure 4).
Explainable artificial intelligence (xAI) project proposed by DARPA [14, 15].
In this structure, unlike the classical deep learning approaches, two different elements draw attention as well as a new machine learning process. One of these is the explanatory model and the other is the explanation interface. The process of deep neural network-based machine learning is explained at the core of the artificial intelligence approach. Among the known deep learning models, autoencoder, convolutional, recurrent (LSTM), deep belief network, or deep reinforcement learning can be preferred. However, it is also possible to use a hybrid structure where several deep learning approaches are used together. Autoencoder-type model of deep neural networks are multilayered perceptron structure. In convolution neural network-type models, layers consist of convolutional layer, ReLU activation function, and max pool layer. A conventional component of the LSTM is composed of a memory cell including input, output, and forget gates. For training, the backpropagation through time algorithm can be preferred. Although the most common form of deep reinforcement learning models is deep Q network (DQN), many different variations of this model can be addressed. Many different algorithms are used as optimization algorithm. Gradient-based algorithms are the most common form of these algorithms (Figure 5).
Deep learning models: (a) autoencoder [17], (b) convolutional neural network [18], and (c) recurrent (LSTM) neural network [19].
Explainable model is an adaptive rule-based reasoning system. It is a structure that reveals the cause-effect relations between input data and the results obtained from the machine learning process. This causal structure learns the rules with its own internal deep learning method. In this way, the explanatory artificial intelligence model allows it to explore the causes and develop new strategies against different situations [20].
The explanation interface is a part of the user interaction. It is similar to the question-answer interface in voice digital assistants. This interface consists of a decoder that evaluates the demands of the user and an encoder unit that enables the responses from the explanatory model, which constitutes the causal mechanism of the explainable artificial intelligence, to the user (Figure 6).
Semantic knowledge matching for explainable artificial intelligence model [21].
In fact, the large networks of semantic technologies (entities) and relationships associated with Knowledge Graphs (KGs) provide a useful solution for the issue of understandability, several reasoning mechanisms, ranging from consistency checking to causal inference [21]. The ontologies realizing these reasoning procedures provide a formal representation of semantic entities and relationships relevant to a particular sphere of knowledge [21]. The input data, hidden layers, encoded features, and predicted output of deep learning models are passed into knowledge graphs (KGs) or concepts and relationships of ontologies (knowledge matching) [21]. Generally, the internal functioning of algorithms to be more transparent and comprehensible can be realized by knowledge matching of deep learning components, including input features, hidden unit and layers, and output predictions with KGs and ontology components [21]. Besides that, the conditions for advanced explanations, cross-disciplinary and interactive explanations are enabled by query and reasoning mechanisms of KGs and ontologies [21].
Although explanatory artificial intelligence forms are of very different structures, all modules such as this explanation interface, explanatory model, and deep learning work in coordination with each other. For example, while a deep learning process estimates classes, such as the explanatory artificial intelligence model (xAI tool) developed by IBM, the concept features data obtained from this process, and another deep learning process using the same input data set produces an explanatory output for the predicted class label output [22] (Figure 7).
Explainable artificial intelligence (xAI) tool developed by IBM [22].
At this point, the explainable artificial intelligence (xAI) tool developed by IBM is referred as a self-explaining neural network (SENN) which can be trained end-to-end with back-propagation in case of that g depends on its arguments in a continuous way [18]. The input is transformed into a small set of interpretable basis features by a concept encoder [22]. The relevance scores are produced by an input-dependent parametrizer. A prediction to be generated is merged by an aggregation function. The full model to behave locally as a linear function on h(x) with parameters
As research and technology on machine learning progresses, artificial intelligence agents consistently display impressive learning performances that meet and exceed the cognitive skills of people in different fields. However, most AI programs are based on computing technology and even reinforcement learning (RL) models that try to regularly improve their knowledge to match human performance. By contrast, people can quickly learn new skills of new skills, simply by having a new skill [23]. The learning of the human brain so efficiently has surprised neuroscientists for years.
In traditional deep learning approaches, the system develops a data-specific model that is transmitted to it by learning from the data. The learning system will perform a certain task only for a certain environment. In the case of another environment, when a very different data is transmitted to it, this deep learning model will be insufficient to perform the task [24]. This issue reveals hard constraints in utilizing machine learning or data mining methods, since the relationship between the learning problem and the effectiveness of different learning algorithms is not yet understood. Under ideal conditions, a system should be designed in which the quality of the data given to the system differs and it can easily adapt to changes in different environments [25]. The deep learning methods used in the current situation are not successful in these situations. At this point, meta-learning, which learns to learn, is an integrated and hierarchical learning model over several different environmental models [26, 27]. As a subfield of machine learning, meta-learning learning algorithms are applied on metadata about machine learning experiments. Instead of classical machine learning approaches that only learn a specific task with single massive dataset, meta-learning is a high-level machine learning approach that learns other tasks together. Therefore, this approach requires a hierarchical structure that learns to learn a new task with distributed hierarchically structured metadata. It is generally applied for hyper parameter adjustment; recent applications have started to focus on a small number of learning. For example, if the system has already learned a few different models or tasks, meta-learning can generalize them and learn how to learn more efficiently. In this way, it can learn new tasks efficiently and create a structure that can easily adapt to changes in multiple tasks in different environments.
People are good at figuring out the meaning of a word after seeing it used only in a few sentences. Similarly, we want our ML algorithms to be generalized to new tasks, without the need for a large data set each time, and to change behavior after a few samples. In typical learning (on a single dataset), each sample targets pair functions as a training point. However, in a small number of learning situations, each “new” sample area is actually another task in itself. In other words, understanding the way that you use unique words in a particular social environment becomes a new task for your language-understanding model, and when you enter a different social environment, it means that the system can adapt to a different language-understanding model than before since it requires to dominate the words that are specific to that social environment. To make sure an ML framework can behave similarly, we have to train it on multiple tasks on its own, so we make each data set a new example of training [28] (Figure 8).
Meta-learning approach [29].
An alternative is to handle the task consecutively as a sequential input array and create a repetitive model that can create a representation of this array for a new task. Typically, in this case, we have a single training process with a memory or attention repetitive network [30]. This approach also gives good results, especially when the installations are properly designed for the task. The calculation performed by the optimizer during the meta-forward transition is very similar to the calculation of a repetitive network [31]. It repeatedly applies the same parameters over a series of inputs (consecutive weights and gradients of the model during learning). In practice, this means that we meet a common problem with repetitive networks. Since the models are not trained to get rid of training errors, they have trouble returning to a safe path when they make mistakes, and the models have difficulty generalizing longer sequences than those used in the order in which they were used. In order to overcome these problems, if the model learns an action policy related to the current educational situation, reinforcement learning approaches can be preferred [32] (Figure 9).
(a) Meta-reinforcement learning (stack of sub-policies representation) [33] and (b) meta-reinforcement learning (inner-outer loop representation) [34].
Formal reinforcement learning algorithm learns a policy for only single task.
In meta-reinforcement learning, there are two distinct processes. One of them is adaptation (inner-loop) behaving ordinary RL policy learning to produce sub-policy where
Another process is meta-training (outer-loop), which is described as meta-policy learning from all sub-policies in the adaptation process (inner-loop).
One of the main differentiers between the human brain and artificial intelligence structures such as deep neural networks, is the brain that utilizes different chemicals known as neurotransmitters to perform different cognitive functions. A new study by DeepMind believes that one of these neurotransmitters plays an important role in the brain\'s ability to quickly learn new topics. Dopamine acts as a reward system that strengthens connections between neurons in the brain.
The DeepMind team has used different meta-reinforcement learning techniques that simulate the role of dopamine in the learning process. Meta-learning trained a repetitive neural network (representing the prefrontal cortex) using standard deep reinforcement learning techniques (representing the role of dopamine) and then compared the activity dynamics of the repetitive network with actual data from previous findings in neuroscience experiments [27]. Recurrent networks are a good example of meta-learning because they can internalize past actions and observations and then use these experiences while training on various tasks.
The meta-learning model recreated the Harlow experiment by saying a virtual computer screen and randomly selected images, and the experiment showed that the “meta-RL agent” was learned in a similar way to the animals found in the Harlow Experiment, even when presented with the Harlow Experiment. All new images were never seen before. The meta-learning agent quickly adapted to different tasks with different rules and structures.
In this section, we will discuss the development of deep reinforcement learning models with an explicable approach to artificial intelligence. Deep reinforcement learning models are machine learning models that learn what action to take according to status and reward information by maximizing reward [27]. Generally, it is widely preferred in robotic, autonomous driverless vehicles, unmanned aerial vehicles, and games. Explanatory artificial intelligence, on the other hand, provides the knowledge of why action should be taken against the situation and reward for deep reinforcement learning models. In this way, it will be possible to gain the causal decision-making ability of the model by revealing the relational links between the input and output of the developed agent (Figure 10).
(a) Reinforcement learning and (b) inverse reinforcement learning [35].
In addition, it is possible to learn the reward derivation mechanism by using the inverse reinforcement learning model [36, 37]. In this case, unlike the previous approach, a meta-cognitive artificial intelligence model that can adapt to other environments instead of just one environment is developed [38, 39]. Taken together with the explainable artificial intelligence approach, it will be possible for the developed agent to develop his own strategy by establishing a cause-effect relationship. For example, the explainable meta-reinforcement learning agent to be developed means that in terms of meta-learning, it can learn to play Go, chess, checkers, and even learn and adapt when it is encountering a new game, and in terms of explainable artificial intelligence, it means that being aware of why it is doing any specific action against a move made by the opponent, it can explain this.
Next generation artificial intelligence structures are expected to have a hierarchical meta-learning ability that can adapt to many different environments, besides being a causal and explanatory power by establishing a cause-effect relationship. For this, serious effort is still needed to create flexible and interpretable models that can hold opinions from many different disciplines together and work in harmony.
We cannot ignore the advantages this will give us. For example, if we start with a medical application, after the patient data is examined, both the physician must understand and explain to the patient why he/she suggested that the explanatory decision support system suggested to the related patient that there was a “risk of heart attack.” At the same time, as a meta-learning agent of this system, it has the same ability against all other diseases and it will be possible to develop appropriate treatment strategies.
While coming to this stage, what data is evaluated first is another important criterion. It is also necessary to explain what data is needed and why, and what is needed for proper evaluation. In the future, next generation deep learning and artificial intelligence forms are expected to reach the level of intelligence (singularity), which has higher performance and ability than human level. Artificial intelligence and deep learning structures mentioned in this section are thought to shed light on reaching these levels. In particular, it can be said that meta-learning approaches are capable of supporting the formation of structures that learn and adapt to multiple tasks and are also called general artificial intelligence (AGI). In the same way, it can be stated that artificial intelligence structures will help the formation of self-awareness and artificial consciousness structures based on content and causality.
The authors declare no conflict of interest.
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\\n\\n*The price does not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate applicable in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT by providing us with their VAT registration number. This is made possible by the EU reverse charge method.
\\n\\nOptional Services
\\n\\nIntechOpen has collaborated with Enago, through its sister company, Ulatus – one of the world’s leading providers of book translation services. The services are designed to convey the essence of your work seamlessly to readers from across the globe in their own language. Enago’s expert translators incorporate cultural nuances in translations to make the content relevant for local audiences while retaining the original meaning and style. With a high degree of linguistic and subject expertise, Enago translators are equipped to handle all complex and multiple overlapping themes encompassed in a single book to deliver a superior quality of translation.
\\n\\nIntechOpen Authors that wish to use this service will receive a 20% discount on all translation work. For more information or a quote, please visit: https://www.enago.com/intech.
\\n\\nFUNDING
\\n\\nWe feel that financial barriers should never prevent researchers from publishing their research. Please consult our Open Access Funding page to explore funding opportunities and learn more about how you can finance your IntechOpen publication.
\\n\\nBENEFITS
\\n\\nPUBLISHING PROCESS STEPS
\\n\\nSee a complete overview and description of the steps involved in the publishing process here.
\\n\\nSEND YOUR PROPOSAL
\\n\\nIf you are interested in publishing your book with IntechOpen, please submit your book proposal by completing the Publishing Proposal Form.
\\n\\nNot sure if this is the right option for you? Please refer back to the main Publish with IntechOpen page or feel free to contact us directly at book.department@intechopen.com
\\n"}]'},components:[{type:"htmlEditorComponent",content:'Without sacrificing the quality of carefully edited and produced peer-reviewed content, Compacts are published as part of IntechOpen’s book collection but on a faster schedule, typically 4-6 weeks after acceptance. With an average of 132,000 visitors per week, publishing in Compacts not only guarantees high visibility but also facilitates international content sharing. As a fully Open Access publisher, the utilization of a CC BY NC 4.0 license means that other researchers will never have to pay permission fees and can adapt, use, and further build upon the material published in Compacts, eliminating any barriers to the further development of scientific research.
\n\nCOMPACTS-SHORT FORM MONOGRAPH
\n\nCOST
\n\n4,000 GBP Compacts Monograph - Short Form
\n\nThe final price will depend on the volume of the publication and includes project management, editorial and peer-review services, technical editing, language copyediting, cover design, book layout, book promotion and ISBN assignment.
\n\n*The price does not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate applicable in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT by providing us with their VAT registration number. This is made possible by the EU reverse charge method.
\n\nOptional Services
\n\nIntechOpen has collaborated with Enago, through its sister company, Ulatus – one of the world’s leading providers of book translation services. The services are designed to convey the essence of your work seamlessly to readers from across the globe in their own language. Enago’s expert translators incorporate cultural nuances in translations to make the content relevant for local audiences while retaining the original meaning and style. With a high degree of linguistic and subject expertise, Enago translators are equipped to handle all complex and multiple overlapping themes encompassed in a single book to deliver a superior quality of translation.
\n\nIntechOpen Authors that wish to use this service will receive a 20% discount on all translation work. For more information or a quote, please visit: https://www.enago.com/intech.
\n\nFUNDING
\n\nWe feel that financial barriers should never prevent researchers from publishing their research. Please consult our Open Access Funding page to explore funding opportunities and learn more about how you can finance your IntechOpen publication.
\n\nBENEFITS
\n\nPUBLISHING PROCESS STEPS
\n\nSee a complete overview and description of the steps involved in the publishing process here.
\n\nSEND YOUR PROPOSAL
\n\nIf you are interested in publishing your book with IntechOpen, please submit your book proposal by completing the Publishing Proposal Form.
\n\nNot sure if this is the right option for you? Please refer back to the main Publish with IntechOpen page or feel free to contact us directly at book.department@intechopen.com
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