05891nam a22005535i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001300153072001600166072002300182072001500205245023600220250001800456264006900474300006500543336002600608337002600634338003600660347002400696490004400720505138700764520232002151650002404471650002004495650002904515650001504544650001704559650002404576650002004600650002604620650002404646650001704670700007604687700008104763700007904844700007704923700007905000710003405079773002605113776003605139776003605175776003605211830004405247856004605291978-3-031-63430-7DE-He21320251030112841.0cr nn 008mamaa240807s2024    sz |    s    |||| 0|eng d  a97830316343077 a10.1007/978-3-031-63430-72doi 4aGB3-5030 7aRGB2bicssc 7aSCI0300002bisacsh 7aRGB2thema10aTransforming Agricultural Management for a Sustainable Futureh[electronic resource] :bClimate Change and Machine Learning Perspectives /cedited by Shruti Kanga, Suraj Kumar Singh, Khetan Shevkani, Vamdev Pathak, Bhartendu Sajan.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aVII, 301 p. 55 illus., 51 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aWorld Sustainability Series,x2199-73810 aChapter1. Understanding the Challenges of Climate Change for Agricultural Management -- Chapter2. Machine Learning Approaches for Crop Yield Prediction -- Chapter3. Data-Driven Decision Making in Agricultural Resource Allocation -- Chapter4. Remote Sensing and Precision Agriculture: A Sustainable Future -- Chapter5. Managing Water Resources for Sustainable Agricultural Production -- Chapter6. Integrating Agroforestry Practices for Climate Change Mitigation and Adaptation -- Chapter7. Exploring the Role of Blockchain in Sustainable Agricultural Management -- Chapter8. Sustainable Soil Management through Advanced Technologies -- Chapter9. Climate-Smart Agricultural Policies for a Sustainable Future -- Chapter10. Promoting Sustainable Agricultural Practices through Farmer-Driven Innovation -- Chapter11. Climate Change Impacts on Crop Productivity and Food Security: An Overview -- Chapter12. Climate change impacts on water resources and implications for agricultural management -- Chapter13. Advanced technologies for sustainable soil management in a changing climate -- Chapter14. Machine learning approaches for improving water management and irrigation efficiency in agriculture -- Chapter15. Machine learning applications for crop disease and pest monitoring and management -- Chapter16. Climate-resilient agroforestry systems for sustainable land use and livelihoods.  a"Transforming Agricultural Management for a Sustainable Future: Climate Change and Machine Learning Perspectives" is an essential read for anyone interested in the future of agriculture and the role that technology can play in mitigating the impact of climate change. The book delves into the challenges facing agriculture today, such as climate change, soil degradation, and water scarcity. It then explores how machine learning can be used to overcome these challenges and promote sustainable agricultural practices. One of the key takeaways from the book is the importance of data-driven decision-making in agriculture. With the help of machine learning algorithms, farmers can analyze vast amounts of data, such as weather patterns, soil quality, and crop yields, to make informed decisions about planting, irrigation, and fertilizer use. By using this data, farmers can optimize their yields while minimizing their impact on the environment. Another important aspect of the book is its focus on climate change. Agriculture is one of the largest contributors to greenhouse gas emissions, and farmers are already feeling the impact of climate change through droughts, floods, and other extreme weather events. The book provides a comprehensive overview of the ways in which machine learning can be used to reduce the impact of agriculture on the environment, such as by optimizing irrigation and reducing fertilizer use. The book also explores the role of technology in promoting sustainable agriculture practices. For example, precision agriculture techniques, such as GPS-guided tractors and drones, can help farmers reduce waste and improve crop yields. The book provides examples of how these techniques are already being used in practice, and how they can be further developed to promote sustainability. Overall, "Transforming Agricultural Management for a Sustainable Future: Climate Change and Machine Learning Perspectives" is an insightful and informative read for anyone interested in the future of agriculture. The book provides a comprehensive overview of the challenges facing agriculture today and the ways in which technology can be used to overcome these challenges and promote sustainable practices. It is a must-read for farmers, policymakers, and anyone interested in the future of our planet. 0aPhysical geography. 0aSustainability. 0aEnvironmental geography. 0aGeography. 0aAgriculture.14aPhysical Geography.24aSustainability.24aIntegrated Geography.24aRegional Geography.24aAgriculture.1 aKanga, Shruti.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aSingh, Suraj Kumar.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aShevkani, Khetan.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aPathak, Vamdev.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aSajan, Bhartendu.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303163429108iPrinted edition:z978303163431408iPrinted edition:z9783031634321 0aWorld Sustainability Series,x2199-738140uhttps://doi.org/10.1007/978-3-031-63430-7