LEADER 00853nam a2200253 i 4500 001 991003187319707536 005 20020509113717.0 008 010514s1956 fr ||| | fre 035 $ab11122353-39ule_inst 035 $aPARLA176824$9ExL 040 $aDip.to Filosofia$bita 082 0 $a194 100 1 $aMillet, Louis$0185931 245 10$aHamelin :$ble système du savoir /$cLouis Millet 260 $aParis :$bPresses universitaires de France,$c1956 300 $a232 p. ;$c18 cm. 440 4$aLes grandes textes 650 4$aHamelin, Octave 907 $a.b11122353$b02-04-14$c28-06-02 912 $a991003187319707536 945 $aLE005IF XXXIII D 3$g1$iLE005IFA-6458$lle005$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i11260270$z28-06-02 996 $aHamelin$9858494 997 $aUNISALENTO 998 $ale005$b01-01-01$cm$da $e-$ffre$gfr $h0$i1 LEADER 05921nam 22007095 450 001 9910879583203321 005 20250808085234.0 010 $a3-031-63430-6 024 7 $a10.1007/978-3-031-63430-7 035 $a(CKB)33734494500041 035 $a(MiAaPQ)EBC31588329 035 $a(Au-PeEL)EBL31588329 035 $a(DE-He213)978-3-031-63430-7 035 $a(EXLCZ)9933734494500041 100 $a20240807d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTransforming Agricultural Management for a Sustainable Future $eClimate Change and Machine Learning Perspectives /$fedited by Shruti Kanga, Suraj Kumar Singh, Khetan Shevkani, Vamdev Pathak, Bhartendu Sajan 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (300 pages) 225 1 $aWorld Sustainability Series,$x2199-7381 311 08$a3-031-63429-2 327 $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. 330 $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. 410 0$aWorld Sustainability Series,$x2199-7381 606 $aPhysical geography 606 $aSustainability 606 $aEnvironmental geography 606 $aGeography 606 $aAgriculture 606 $aPhysical Geography 606 $aSustainability 606 $aIntegrated Geography 606 $aRegional Geography 606 $aAgriculture 615 0$aPhysical geography. 615 0$aSustainability. 615 0$aEnvironmental geography. 615 0$aGeography. 615 0$aAgriculture. 615 14$aPhysical Geography. 615 24$aSustainability. 615 24$aIntegrated Geography. 615 24$aRegional Geography. 615 24$aAgriculture. 676 $a910.02 700 $aKanga$b Shruti$01737514 701 $aSingh$b Suraj Kumar$01737513 701 $aShevkani$b Khetan$01765110 701 $aPathak$b Vamdev$01765111 701 $aSajan$b Bhartendu$01765112 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910879583203321 996 $aTransforming Agricultural Management for a Sustainable Future$94206351 997 $aUNINA