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Weather Patterns and Machine Learning

In: Machine Learning and Artificial Intelligence for Agricultural Economics

Author

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  • Chandrasekar Vuppalapati

    (San Jose State University)

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)).

Suggested Citation

  • Chandrasekar Vuppalapati, 2021. "Weather Patterns and Machine Learning," International Series in Operations Research & Management Science, in: Machine Learning and Artificial Intelligence for Agricultural Economics, chapter 0, pages 331-428, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-77485-1_5
    DOI: 10.1007/978-3-030-77485-1_5
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