Weather Patterns and Machine Learning

Chandrasekar Vuppalapati ()
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Chandrasekar Vuppalapati: San Jose State University

Chapter Chapter 5 in Machine Learning and Artificial Intelligence for Agricultural Economics, 2021, pp 331-428 from Springer

Abstract: Abstract In this chapter, the effects of climate change and changing temperatures on agricultural commodities are introduced. As part of the chapter, two use cases and ML are introduced: rice production in Sacramento valley and milk production in the United States and international. The role of crop calendar, the minimum and maximum temperature on the rice yield, the precipitation, and the effects of drought are introduced, as well as the effects of macrolevel economic indicators on milk production and of raising temperatures on milk productivity and, finally, the application of weather events and modeling ML models, by taking into account the effects of temperature, precipitation, and drought index (Palmer Drought Severity Index (PDSI)).

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-77485-1_5

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DOI: 10.1007/978-3-030-77485-1_5

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