國立高雄大學圖資館 |

Transforming agricultural management for a sustainable futureclimate change and machine learning perspectives /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Transforming agricultural management for a sustainable futureedited by Shruti Kanga ...[et al.].
Reminder of title:	climate change and machine learning perspectives /
other author:	Kanga, Shruti.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	vii, 301 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Sustainable agriculture.
Online resource:	https://doi.org/10.1007/978-3-031-63430-7
ISBN:	9783031634307$q(electronic bk.)

Transforming agricultural management for a sustainable futureclimate change and machine learning perspectives /
Transforming agricultural management for a sustainable futureclimate change and machine learning perspectives /[electronic resource] :edited by Shruti Kanga ...[et al.]. - Cham :Springer Nature Switzerland :2024. - vii, 301 p. :ill. (some col.), digital ;24 cm. - World sustainability series,2199-7381. - World sustainability series..

Chapter1. 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.

"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.

ISBN: 9783031634307$q(electronic bk.)

Standard No.: 10.1007/978-3-031-63430-7doiSubjects--Topical Terms:

277356
Sustainable agriculture.

LC Class. No.: S494.5.S86

Dewey Class. No.: 630.2086

Transforming agricultural management for a sustainable futureclimate change and machine learning perspectives /
LDR:04862nmm a22003495a 4500 001 667753
003 DE-He213
005 20240807130245.0
006 m d
007 cr nn 008maaau
008 250107s2024 sz s 0 eng d
020 $a 9783031634307$q(electronic bk.)
020 $a 9783031634291$q(paper)
024 7 $a 10.1007/978-3-031-63430-7 $2 doi
035 $a 978-3-031-63430-7
040 $a GP $c GP
041 0 $a eng
050 4 $a S494.5.S86
072 7 $a RGB $2 bicssc
072 7 $a SCI030000 $2 bisacsh
072 7 $a RGB $2 thema
082 0 4 $a 630.2086 $2 23
090 $a S494.5.S86 $b T772 2024
245 0 0 $a Transforming agricultural management for a sustainable future $h [electronic resource] : $b climate change and machine learning perspectives / $c edited by Shruti Kanga ...[et al.].
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a vii, 301 p. : $b ill. (some col.), digital ; $c 24 cm.
347 $a text file $b PDF $2 rda
490 1 $a World sustainability series, $x 2199-7381
505 0 $a Chapter1. 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.
520 $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.
650 0 $a Sustainable agriculture. $3 277356
650 1 4 $a Physical Geography. $3 212466
650 2 4 $a Sustainability. $3 376755
650 2 4 $a Integrated Geography. $3 913557
650 2 4 $a Regional Geography. $3 913517
650 2 4 $a Agriculture. $3 274257
700 1 $a Kanga, Shruti. $3 919229
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a World sustainability series. $3 710702
856 4 0 $u https://doi.org/10.1007/978-3-031-63430-7
950 $a Earth and Environmental Science (SpringerNature-11646)