The document discusses how subseasonal to seasonal forecasts can help drive analytics in the cattle industry. It provides examples of how forecasts of drought in the Southern Plains and Hurricane Harvey helped cattle producers plan grazing and reduce exposure to weather risks. Livestock Wx translates weather and climate information for cattle producers and uses probabilistic precipitation forecasts from multiple models to assess risk on timescales from monthly to interannual. Subseasonal to seasonal predictions have economic value for the cattle industry by helping with tactical planning for issues like forage growth, pest outbreaks, and cattle weights.

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. 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. THE OCEANS & FORECASTING • 2018
Subseasonal to Seasonal Forecasts
Driving Analytics
Drought in the Southern
Plains
Hurricane Harvey

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Grazing & Winter Wheat in Southern
Plains

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Winter Wheat Production & La Niña

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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

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Modeling Risk
Monthly Forecast
Demographic/production data

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Tactical Planning for Hurricane
Harvey
August 27, 2017 Dayton, TX

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“1.2 million head of cattle could be
affected by the storm”

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• 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