Release Date: 2024-06-07

Artificial Intelligence (Al) Applications to Reduce Drought Loss in Agriculture

Muhammad Abu Bakar Zia (Author), Sadettin Celik (Author), Mehmet Vural (Author)

Release Date: 2024-06-07

Medicinal aromatic plants are very important for human health. Due to the polyphenolic and phytochemical components they contain, they have antioxidant, antibacterial, anticancer, antiviral and anti-inflammatory properties. For this reason, it is the subject of scientific studies. Allium species are important medicinal and aromatic plants that include the commonly known onion and garlic species. There [...]

Media Type
    Buy from

    Price may vary by retailers

    Work TypeBook Chapter
    Published inRecent Applications and Biological Activities in Aquaculture and Agriculture
    First Page191
    Last Page206
    DOIhttps://doi.org/10.69860/nobel.9786053359319.10
    Page Count16
    Copyright HolderNobel Tıp Kitabevleri
    Licensehttps://nobelpub.com/publish-with-us/copyright-and-licensing

    Medicinal aromatic plants are very important for human health. Due to the polyphenolic and phytochemical components they contain, they have antioxidant, antibacterial, anticancer, antiviral and anti-inflammatory properties. For this reason, it is the subject of scientific studies. Allium species are important medicinal and aromatic plants that include the commonly known onion and garlic species. There are many different species and the different biological activities of these species are determined by scientific studies. For this reason, in this chapter, the ethnobotanical characteristics and biological activities of important Allium species with current scientific studies are compiled. It is stated that some of these species have anticancer properties and their consumption will be beneficial for health.

    Muhammad Abu Bakar Zia (Author)
    Bingöl University
    https://orcid.org/0000-0002-7685-1392
    3Dr Muhammad Abu Bakar Zia, Who Graduated from Nigde Omer Halisdemir University Nigde Turkey in 2018. He Got GOLD MEDAL in his Master studies. He currently works as Assistant Professor in University of Layyah, Pakistan on Part time basis. His doctoral research was on Genome wide Association studies in Potato

    Sadettin Celik (Author)
    Bingöl University
    https://orcid.org/0000-0002-8396-4627
    3Sadettin Çelik completed his doctorate in the field of Plant Biotechnology within the Department of Agricultural Biotechnology. His doctoral thesis focused on determining DNA markers associated with Verticillium wilt disease in cotton through association mapping analysis and the genotyping by sequencing method. Dr. Sadettin Çelik, who is currently working in the Department of Forestry, conducts research in areas such as drought, genetic diversity, Marker Assisted Selection (MAS), Next Generation Sequencing (NGS), Genotyping-by-Sequencing (GBS), Association Mapping, QTL Mapping, bioinformatics, classical and molecular plant breeding, and abiotic and biotic stress

    Mehmet Vural (Author)
    Bingöl University
    https://orcid.org/0000-0002-5973-4856
    3Mehmet Vural graduated with a bachelor’s degree in software engineering. He completed his master’s degree in mechatronics engineering in the field of computer systems. He is a faculty member in the Information Security department at Bingöl University, Genç Vocational School. He is proficient in artificial intelligence, cybersecurity, information security, cryptology, deep learning, machine learning, and programming languages. He is also conducting artificial intelligence studies on drought stress in plants.

    • Beguer´ıa, S., S. M. Vicente-Serrano, and M. Angulo-Mart´ınez, 2010: A multiscalar global drought dataset: The SPEIbase: A new gridded product for the analysis of drought variability and impacts. Bull. Amer. Meteor. Soc., 91, 1351–1356.

    • Kuwayama, Y., Thompson, A., Bernknopf, R., Zaitchik, B., & Vail, P. (2019). Estimating the impact of drought on agriculture using the US Drought Monitor. American Journal of Agricultural Economics, 101(1), 193-210.

    • Keykhasaber, M., Thomma, B.P.H.J. & Hiemstra, J.A. Distribution and persistence of Verticillium dahliae in the xylem of Norway maple and European ash trees. Eur J Plant Pathol 150, 323–339 (2018). https://doi.org/10.1007/s10658-017-1280-z.

    • Kashyap, A., Planas-Marquès, M., Capellades, M., Valls, M., & Coll, N. S. (2021). Blocking intruders: inducible physico-chemical barriers against plant vascular wilt pathogens. Journal of experimental botany, 72(2), 184–198. https://doi. org/10.1093/jxb/eraa444.

    • Çelik, S. (2024). Screening Some Advanced Upland Cotton (Gossypium Hirsutum L.) Genotypes Tolerance Under Water Deficit. Turkish Journal of Nature and Science, 13(1), 104-110.

    • Çelik, S. (2020). Bazı Upland Pamuk (Gossypium hirsutum L.) Çeşitlerinin Çimlenme Döneminde Farklı Tuz (NaCI) Seviyelerine Karşı Toleranslarının Belirlenmesi. Türk Doğa ve Fen Dergisi, 9(2), 112-117.

    • Celik, S. (2024 Polyethylene Glycol 6000 induced upland cotton (gossypium hirsutum l.) Cultivars’ drought response at the germination stage. Uluslararası Gıda Tarım ve Hayvan Bilimleri Dergisi, 4(1), 1-9.

    • Çelik, A. P. S (2022). Marker-assısted selectıon (mas) technology for developıng tolerance cotton varıety agaınst drought. Engıneerıng and archıtecture scıences, 144.

    • Spinoni, J., Naumann, G., Vogt, J., & Barbosa, P. (2016). Meteorological droughts in Europe: events and impacts-past trends and future projections. Publications Office of the European Union.

    • Ward, F. A., & Pulido-Velazquez, M. (2008). Water conservation in irrigation can increase water use. Proceedings of the National Academy of Sciences, 105(47), 18215-18220.

    • Kjelgren, R., Rupp, L., & Kilgren, D. (2000). Water conservation in urban landscapes. HortScience, 35(6), 1037-1040.

    • Pereira, L. S., Cordery, I., & Iacovides, I. (2012). Improved indicators of water use performance and productivity for sustainable water conservation and saving. Agricultural water management, 108, 39-51.

    • Critchley, W., Siegert, K., Chapman, C., & Finket, M. (2013). Water harvesting: A manual for the design and construction of water harvesting schemes for plant production. Scientific Publishers.

    • Tu, Y., Wang, R., Zhang, Y., & Wang, J. (2018). Progress and expectation of atmospheric water harvesting. Joule, 2(8), 1452-1475.

    • Dobriyal, P., Qureshi, A., Badola, R., & Hussain, S. A. (2012). A review of the methods available for estimating soil moisture and its implications for water resource management. Journal of Hydrology, 458, 110-117.

    • Fipps, G. (1990). Soil Moisture Management. Bulletin/Texas Agricultural Extension Service; no. 1670.

    • Hanson, B., Orloff, S., & Peters, D. (2000). Monitoring soil moisture helps refine irrigation management. California Agriculture, 54(3), 38-42.

    • Irmak, S., Burgert, M. J., Yang, H. S., Cassman, K. G., Walters, D. T., Rathje, W. R., ... & Teichmeier, G. J. (2012). Large-scale on-farm implementation of soil moisture-based irrigation management strategies for increasing maize water productivity. Transactions of the ASABE, 55(3), 881-894.

    • Silva, B. M., Santos, W. J. R. D., Oliveira, G. C. D., Lima, J. M. D., Curi, N., & Marques, J. J. (2015). Soil moisture space-time analysis to support improved crop management. Ciência e Agrotecnologia, 39, 39-47.

    • Silva, B. M., Oliveira, G. C., Serafim, M. E., Silva, É. A., Guimarães, P. T. G., Melo, L. B. B., ... & Curi, N. (2019). Soil moisture associated with least limiting water range, leaf water potential, initial growth and yield of coffee as affected by soil management system. Soil and Tillage Research, 189, 36-43.

    • Degani, E., Leigh, S. G., Barber, H. M., Jones, H. E., Lukac, M., Sutton, P., & Potts, S. G. (2019). Crop rotations in a climate change scenario: short-term effects of crop diversity on resilience and ecosystem service provision under drought. Agriculture, Ecosystems & Environment, 285, 106625.

    • Louis Baumhardt, R., & Anderson, R. L. (2006). Crop choices and rotation principles. Dryland agriculture, 23, 113-139.

    • Hlavinka, P., Kersebaum, K. C., Dubrovský, M., Fischer, M., Pohanková, E., Balek, J., & Trnka, M. (2015). Water balance, drought stress and yields for rainfed field crop rotations under present and future conditions in the Czech Republic. Climate Research, 65, 175-192.

    • Shah, K. K., Modi, B., Pandey, H. P., Subedi, A., Aryal, G., Pandey, M., & Shrestha, J. (2021). Diversified crop rotation: an approach for sustainable agriculture production. Advances in Agriculture, 2021, 1-9.

    • Jacobsen, S. E., Jensen, C. R., & Liu, F. (2012). Improving crop production in the arid Mediterranean climate. Field Crops Research, 128, 34-47.

    • Nielsen, D. C., Vigil, M. F., & Benjamin, J. G. (2011). Evaluating decision rules for dryland rotation crop selection. Field Crops Research, 120(2), 254-261.

    • FAO, 2012. Sustainable Crop Production Intensification through Improved Irrigation and Fertilizer Practices: http://www.fao.org/3/a-i3137e.pdf

    • IWMI, 2024. International Water Management Institute Irrigation and Water Management (IWMI): https://www.iwmi.cgiar.org/research/themes/water-management/

    • Haefele, S. M., Kato, Y., & Singh, S. (2016). Climate ready rice: augmenting drought tolerance with best management practices. Field Crops Research, 190, 60-69.

    • Tirado, R., & Cotter, J. (2010). Ecological farming: Drought-resistant agriculture. Exeter, UK: Greenpeace Research Laboratories.

    • McFadden, J., Smith, D., Wechsler, S., & Wallander, S. (2019). Development, adoption, and management of drought-tolerant corn in the United States.

    • Fisher, M., Abate, T., Lunduka, R. W., Asnake, W., Alemayehu, Y., & Madulu, R. B. (2015). Drought tolerant maize for farmer adaptation to drought in sub-Saharan Africa: Determinants of adoption in eastern and southern Africa. Climatic Change, 133, 283-299.

    • Wilhite, D. A. (1993). Planning for drought: A methodology. In Drought assessment, management, and planning: Theory and case studies (pp. 87-108). Boston, MA: Springer US.

    • Wilhite, D. A. (2002, November). Combating drought through preparedness. In Natural resources forum (Vol. 26, No. 4, pp. 275-285). Oxford, UK and Boston, USA: Blackwell Publishing Ltd.

    • Wilhite, D. A., Sivakumar, M. V., & Pulwarty, R. (2014). Managing drought risk in a changing climate: The role of national drought policy. Weather and climate extremes, 3, 4-13.

    • Roy, R. N., Kundu, S., & Kumar, R. S. (2021). The impacts and evidence of Australian droughts on agricultural crops and drought related policy issues-a review article.

    • Garrido, A., & Gómez-Ramos, A. (2009). Risk management instruments supporting drought planning and policy. Coping with Drought Risk in Agriculture and Water Supply Systems: Drought Management and Policy Development in the Mediterranean, 133-151.

    • Miao, R. (2020). Climate, insurance and innovation: the case of drought and innovations in drought-tolerant traits in US agriculture. European Review of Agricultural Economics, 47(5), 1826-1860.

    • Dutta, V., Vimal, M., Singh, S., & Singh, R. P. (2019). Agricultural practices in a drought-prone region of India: opportunities for S&T innovations. World Journal of Science, Technology and Sustainable Development, 16(4), 208-226.

    • Enenkel, M., See, L., Bonifacio, R., Boken, V., Chaney, N., Vinck, P., ... & Anderson, M. (2015). Drought and food security–Improving decision-support via new technologies and innovative collaboration. Global Food Security, 4, 51-55.

    • Chami, D. E., & Moujabber, M. E. (2016). Drought, climate change and sustainability of water in agriculture: A roadmap towards the NWRS2. South African Journal of Science, 112(9-10), 1-4.

    • Samuel, J., Rao, C. A. R., Raju, B. M. K., Reddy, A. A., Pushpanjali, Reddy, A. G. K., ... & Prasad, J. V. N. S. (2021). Assessing the impact of climate resilient technologies in minimizing drought impacts on farm incomes in drylands. Sustainability, 14(1), 382.

    • Rey, D., Holman, I. P., & Knox, J. W. (2017). Developing drought resilience in irrigated agriculture in the face of increasing water scarcity. Regional environmental change, 17, 1527-1540.

    • Reubens, B., Poesen, J., Danjon, F., Geudens, G., & Muys, B. (2007). The role of fine and coarse roots in shallow slope stability and soil erosion control with a focus on root system architecture: a review. Trees, 21(4), 385-402.

    • Stokes, A., Atger, C., Bengough, A. G., Fourcaud, T., & Sidle, R. C. (2009). Desirable plant root traits for protecting natural and engineered slopes against landslides. Plant and soil, 324, 1-30.

    • Gyssels, G., & Poesen, J. (2003). The importance of plant root characteristics in controlling concentrated flow erosion rates. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 28(4), 371384.

    • Morgan, R. P. C. (2009). Soil erosion and conservation. John Wiley & Sons.

    • Hairiah, K., Widianto, W., Suprayogo, D., & Van Noordwijk, M. (2020). Tree roots anchoring and binding soil: Reducing landslide risk in Indonesian agroforestry. Land, 9(8), 256.

    • Koevoets, I. T., Venema, J. H., Elzenga, J. T. M., & Testerink, C. (2016). Roots withstanding their environment: exploiting root system architecture responses to abiotic stress to improve crop tolerance. Frontiers in plant science, 7, 1335.

    • Passioura, J. B. (1983). Roots and drought resistance. In Developments in agricultural and managed forest ecology (Vol. 12, pp. 265-280). Elsevier.

    • Miller, D. E. (1986). Root systems in relation to stress tolerance. HortScience, 21(4), 963-970.

    • Comas, L. H., Becker, S. R., Cruz, V. M. V., Byrne, P. F., & Dierig, D. A. (2013). Root traits contributing to plant productivity under drought. Frontiers in plant science, 4, 442.

    • Ho, M. D., McCannon, B. C., & Lynch, J. P. (2004). Optimization modeling of plant root architecture for water and phosphorus acquisition. Journal of Theoretical Biology, 226(3), 331-340.

    • Lynch, J. (1995). Root architecture and plant productivity. Plant physiology, 109(1), 7.

    • Hodge, A., Berta, G., Doussan, C., Merchan, F., & Crespi, M. (2009). Plant root growth, architecture and function.

    • Gregory, P. J., & Wojciechowski, T. (2020). Root systems of major tropical root and tuber crops: Root architecture, size, and growth and initiation of storage organs. Advances in Agronomy, 161, 1-25.

    • Passot, S., Gnacko, F., Moukouanga, D., Lucas, M., Guyomarc’h, S., Ortega, B. M., ... & Laplaze, L. (2016). Characterization of pearl millet root architecture and anatomy reveals three types of lateral roots. Frontiers in plant science, 7, 829.

    • Wasson, A. P., Richards, R. A., Chatrath, R., Misra, S. C., Prasad, S. S., Rebetzke, G. J., ... & Watt, M. (2012). Traits and selection strategies to improve root systems and water uptake in water-limited wheat crops. Journal of experimental botany, 63(9), 3485-3498.

    • Collins, D. B. G., & Bras, R. L. (2007). Plant rooting strategies in water-limited ecosystems. Water Resources Research, 43(6).

    • Gregory, P. (2007). Plant roots. John Wiley & Sons, Limited.

    • Zia, R., Nawaz, M. S., Siddique, M. J., Hakim, S., & Imran, A. (2021). Plant survival under drought stress: Implications, adaptive responses, and integrated rhizosphere management strategy for stress mitigation. Microbiological research, 242, 126626.

    • Obidiegwu, J. E., Bryan, G. J., Jones, H. G., & Prashar, A. (2015). Coping with drought: stress and adaptive responses in potato and perspectives for improvement. Frontiers in plant science, 6, 151023.

    • Neumann, P. M. (2008). Coping mechanisms for crop plants in drought-prone environments. Annals of Botany, 101(7), 901-907.

    • Ilyas, M., Nisar, M., Khan, N., Hazrat, A., Khan, A. H., Hayat, K., ... & Ullah, A. (2021). Drought tolerance strategies in plants: a mechanistic approach. Journal of Plant Growth Regulation, 40, 926-944.

    • McCarthy, J. What is artifical intelligence? (2004). http://faculty.otterbein.edu/ dstucki/inst4200/whatisai.pdf, Last accessed on 2022-09-12.

    • Kumar, K., & Thakur, G. S. M. (2012). Advanced applications of neural networks and artificial intelligence: A review. International journal of information technology and computer science, 4(6), 57.

    • Horáková, T., Houška, M., & Dömeová, L. (2017). Classification of the educational texts styles with the methods of artificial intelligence. Journal of Baltic Science Education, 16(3), 324.

    • Dharmaraj, V., & Vijayanand, C. (2018). Artificial intelligence (AI) in agriculture. International Journal of Current Microbiology and Applied Sciences, 7(12), 2122-2128.

    • Hassani, H.; Silva, E.S.; Unger, S.; TajMazinani, M.; Mac Feely, S. Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is the Future? AI 2020, 1, 143-155. https://doi.org/10.3390/ai1020008.

    • Talaviya, T., Shah, D., Patel, N., Yagnik, H., & Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4, 58-73.

    • Ben Ayed, R., & Hanana, M. (2021). Artificial intelligence to improve the food and agriculture sector. Journal of Food Quality, 2021, 1-7.

    • Liu, S. Y. (2020). Artificial intelligence (AI) in agriculture. IT professional, 22(3), 14-15.

    • Smith, M. J. (2018). Getting value from artificial intelligence in agriculture. Animal Production Science, 60(1), 46-54.

    • Bannerjee, G., Sarkar, U., Das, S., & Ghosh, I. (2018). Artificial intelligence in agriculture: A literature survey. international Journal of Scientific Research in computer Science applications and Management Studies, 7(3), 1-6.

    • Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem, 2(1), 15-30.

    • Wang, Z., Liu, Y., & Niu, X. (2023, April). Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology. In Seminars in Cancer Biology. Academic Press.

    • Kwon, J. M., Kim, K. H., Jo, Y. Y., Jung, M. S., Cho, Y. H., Shin, J. H., ... & Oh, B. H. (2022). Artificial intelligence assessment for early detection and prediction of renal impairment using electrocardiography. International Urology and Nephrology, 54(10), 2733-2744.

    • Goh, K. H., Wang, L., Yeow, A. Y. K., Poh, H., Li, K., Yeow, J. J. L., & Tan, G. Y. H. (2021). Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nature communications, 12(1), 711.

    • Ghaffar Nia, N., Kaplanoglu, E., & Nasab, A. (2023). Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discover Artificial Intelligence, 3(1), 5.

    • Xiang, X., Li, Q., Khan, S., & Khalaf, O. I. (2021). Urban water resource management for sustainable environment planning using artificial intelligence techniques. Environmental Impact Assessment Review, 86, 106515.

    • Pinto, A., Fernandes, A., Vicente, H., & Neves, J. (2009). Optimizing water treatment systems using artificial intelligence based tools. WIT Transactions on Ecology and the Environment, 125, 185-194.

    • Tariq, R., Cetina-Quiñones, A. J., Cardoso-Fernández, V., Daniela-Abigail, H. L., Soberanis, M. E., Bassam, A., & De Lille, M. V. (2021). Artificial intelligence assisted technoeconomic optimization scenarios of hybrid energy systems for water management of an isolated community. Sustainable Energy Technologies and Assessments, 48, 101561.

    • Jahandideh-Tehrani, M., Bozorg-Haddad, O., & Loáiciga, H. A. (2020). Application of particle swarm optimization to water management: an introduction and overview. Environmental Monitoring and Assessment, 192(5), 281.

    • Soni, D., Patel, P., & Shah, M. (2022). Artificial intelligence in crop monitoring. In Agricultural Biotechnology (pp. 247-257). CRC Press.

    • Shankar, P., Werner, N., Selinger, S., & Janssen, O. (2020, September). Artificial intelligence driven crop protection optimization for sustainable agriculture. In 2020 IEEE/ ITU International Conference on Artificial Intelligence for Good (AI4G) (pp. 1-6). IEEE.

    • Elsalahy, H. H., Bellingrath-Kimura, S. D., Roß, C. L., Kautz, T., & Döring, T. F. (2020). Crop resilience to drought with and without response diversity. Frontiers in Plant Science, 11, 529619.

    • Backhaus, A. E., Jimenez, J. A., Visioni, A., & Sanchez-Garcia, M. (2023). Unveiling Drought-Resilient Pathways: Integrating High Throughput Phenotyping and Multivariate Modeling to Enhance Barley Adaptation to Climate Change.

    • de Vries, F. T., Griffiths, R. I., Knight, C. G., Nicolitch, O., & Williams, A. (2020). Harnessing rhizosphere microbiomes for drought-resilient crop production. Science, 368(6488), 270-274.

    • Zhai, Z., Martínez, J. F., Beltran, V., & Martínez, N. L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, 105256.

    • Arshad, J., Aziz, M., Al-Huqail, A. A., Zaman, M. H. U., Husnain, M., Rehman, A. U., & Shafiq, M. (2022). Implementation of a LoRaWAN based smart agriculture decision support system for optimum crop yield. Sustainability, 14(2), 827.

    • Navarro-Hellín, H., Martinez-del-Rincon, J., Domingo-Miguel, R., Soto-Valles, F., & Torres-Sánchez, R. (2016). A decision support system for managing irrigation in agriculture. Computers and Electronics in Agriculture, 124, 121-131.

    • Dabre, K. R., Lopes, H. R., & D’monte, S. S. (2018, January). Intelligent decision support system for smart agriculture. In 2018 International Conference on Smart City and Emerging Technology (ICSCET) (pp. 1-6). IEEE.

    • Kamyab, H., Khademi, T., Chelliapan, S., SaberiKamarposhti, M., Rezania, S., Yusuf, M., ... & Ahn, Y. (2023). The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management. Results in Engineering, 101566.

    • Tang, H. W., Lei, Y., Lin, B., Zhou, Y. L., & Gu, Z. H. (2010, April). Artificial intelligence model for water resources management. In Proceedings of the Institution of Civil Engineers-Water Management (Vol. 163, No. 4, pp. 175-187). Thomas Telford Ltd.

    • Parmar, S. P. (2023). Water Resource Management Using Artificial Intelligence Enabled RS & GIS. Journal of Water Resource Research and Development, 6(1), 29-41.

    • Saxena, R., Srivastava, V., Bharti, D., Singh, R., Kumar, A., & Sharma, A. (2024). Artificial Intelligence for Water Resource Planning and Management. In Innovations in Machine Learning and IoT for Water Management (pp. 51-70). IGI Global.

    • Sudhakar, M. (2023). Artificial Intelligence Applications in Water Treatment and Water Resource Assessment: Challenges, Innovations, and Future Directions. In Intelligent Engineering Applications and Applied Sciences for Sustainability (pp. 248-269). IGI Global.

    • Eli-Chukwu, N. C. (2019). Applications of artificial intelligence in agriculture: A review. Engineering, Technology & Applied Science Research, 9(4).

    • Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18(1), 100330.

    • Uddin, M., Chowdhury, A., & Kabir, M. A. (2024). Legal and ethical aspects of deploying artificial intelligence in climate-smart agriculture. AI & SOCIETY, 39(1), 221-234.

    onix_3.0::thothThoth ONIX 3.0
    onix_3.0::project_museProject MUSE ONIX 3.0
    onix_3.0::oapenOAPEN ONIX 3.0
    onix_3.0::jstorJSTOR ONIX 3.0
    onix_3.0::google_booksGoogle Books ONIX 3.0
    onix_3.0::overdriveOverDrive ONIX 3.0
    onix_2.1::ebsco_hostEBSCO Host ONIX 2.1
    csv::thothThoth CSV
    json::thothThoth JSON
    kbart::oclcOCLC KBART
    bibtex::thothThoth BibTeX
    doideposit::crossrefCrossRef DOI deposit
    onix_2.1::proquest_ebraryProQuest Ebrary ONIX 2.1
    marc21record::thothThoth MARC 21 Record
    marc21markup::thothThoth MARC 21 Markup
    marc21xml::thothThoth MARC 21 XML

    Share This Chapter!