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How Subseasonal to Seasonal Forecasts Drive Analytics in the Cattle Industry

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Chad McNutt, Cofounder, Livestock Wx

UCAR Congressional Briefing - April 2018

Published in: Science
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How Subseasonal to Seasonal Forecasts Drive Analytics in the Cattle Industry

  1. 1. How Subseasonal to Seasonal Forecasts Drive Analytics in the Cattle Industry Hosted by the University Corporation for Atmospheric Research • April 24, 2018 Chad McNutt Cofounder, Livestock Wx
  2. 2. THE OCEANS & FORECASTING • 2018 v Translate weather and climate information for livestock producers v Founded 2017 v Subseasonal to seasonal predictions can play an important role in reducing cattle producers’ exposure to weather and climate variability v Forecast value limited by the extent it can be contextualized Livestock Wx
  3. 3. THE OCEANS & FORECASTING • 2018 Subseasonal to Seasonal Forecasts Driving Analytics Drought in the Southern Plains Hurricane Harvey
  4. 4. THE OCEANS & FORECASTING • 2018 Grazing & Winter Wheat in Southern Plains
  5. 5. THE OCEANS & FORECASTING • 2018 Winter Wheat Production & La Niña
  6. 6. THE OCEANS & FORECASTING • 2018 Standardized Precipitation Index Forecast • Probabilistic forecasts of the Standardized Precipitation Index (SPI) based on monthly precipitation predictions from 6 NMME models • SPI is an important indicator, computed on subseasonal to interannual time scales • Useful in both short-term and long- term applications because different time scales reflect different impacts
  7. 7. THE OCEANS & FORECASTING • 2018 Modeling Risk Monthly Forecast Demographic/production data
  8. 8. THE OCEANS & FORECASTING • 2018 Tactical Planning for Hurricane Harvey August 27, 2017 Dayton, TX
  9. 9. THE OCEANS & FORECASTING • 2018 “1.2 million head of cattle could be affected by the storm”
  10. 10. THE OCEANS & FORECASTING • 2018 • Need sustained support of S2S efforts like the NMME • Has real economic implications for ag sectors like the cattle industry • Drought accelerating cattle supply • Cattle Futures were up ~2.5% following Harvey’s landfall • Two simple examples. S2S predictions could be extended to other analytics, e.g. • Seasonal forage growth • Timing of pests and commercial pesticide pre-deployment • Improved estimates of fed cattle carcass weights Summary

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