ML Models: Food Security and Climate Change

Chandrasekar Vuppalapati
Additional contact information
Chandrasekar Vuppalapati: San Jose State University

Chapter Chapter 6 in Artificial Intelligence and Heuristics for Enhanced Food Security, 2022, pp 395-518 from Springer

Abstract: Abstract The chapter introduces the impact of climate change on agriculture productions, develops a framework to identify countries that require immediate action to develop climate-smart agriculture, and establishes a machine learning model to develop such recommendations. As part of identification of countries that are highly sensitive and need immediate action, the chapter studies climate change framework and countries that are under food security emergencies plus the Global Information and Early Warning System on Food and Agriculture (GIEWS). Next, it deep dives on the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate projections and develops Shared Socioeconomic Pathways (challenges to adaptation vs. mitigation) framework. Finally, the chapter concludes with the development of machine learning models for Thailand rice paddy yields and Vietnam coffee yields and simulates the food production for SSP 2080–2099.

Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-031-08743-1_6

Ordering information: This item can be ordered from
http://www.springer.com/9783031087431

DOI: 10.1007/978-3-031-08743-1_6

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().