Atmospheric warming leads to increased weather variability both over space and in-time (extremes).  The whole of the agricultural value chain needs agronomically interpreted real-time weather based information to dampen this impact.  From input providers to farmers and markets, ag-weather is an integral need. Predictive analytics delivers insight to farmers and in aggregate, to other segments in the value chain. 

Weather will always play a significant role in agriculture, but its impacts and forcing are not always clear and its predictability will always carry uncertainty.  As weather data continually improves, there will be a push to further integrate it with other datasets to develop more optimal insights and improve decision making.  MDA has provided weather information in addition to soil, imagery, and other remote sensing data to customers for nearly 40 years – and now looks to deepen its analytics by leveraging the latest in technology AND integrating our customers’ feedback into premier agricultural solutions.  Through state-of-the-science modeling and a deep suite of environmental monitoring capabilities at relevant levels and resolutions, MDA continues to develop new ways of marrying weather data – observed, real time, and forecasts - to our continually evolving high-value remote sensing capabilities and exploiting our scientific skill sets with a focus on developing tools that matter.

The talk will focus on a better paradigm than using the same weather parameters we have traditionally been able to measure with weather stations, where the user is using mental “algorithms” to translate the weather info into decisions.  For example, using rainfall data in conjunction with potential evapotranspiration values (which in turn are often derived from simplistic assumptions that can now be explicitly modeled) as an indicator of water availability for the crop.  In this case, the  user is really needing to know whether there is enough moisture in the soil for the plants to access, and that can be explicitly modeled with more complicated physical equations, making the guidance more relevant and accurate.  Other examples will be provided (spraying, harvesting, etc.).

During this panel discussion, Chacko Jacob will highlight the latest innovations in advanced weather models, analytics and provide a glimpse of what the future holds for decision support and artificial intelligence solutions within Agriculture. Weather not only affects how crops grow but the logistics around planting, harvesting, and transportation. By integrating weather forecast models into crop planting and harvesting and transportation, better decisions can be made in advance of crop losses due to weather perils. Weather data and analytics on field-by-field or zone-by-zone basis helps farmers make informed decisions throughout the year to maximize food production, minimize environmental impact, and reduce operating costs.

The Climate Corporation, a subsidiary of Monsanto Company, aims to help all the world’s farmers sustainably increase their productivity through the use of digital tools. The integrated Climate FieldView™ digital agriculture platform provides farmers with a comprehensive, connected suite of digital tools. Bringing together seamless field data collection, advanced agronomic modeling and local weather monitoring into simple mobile and web software solutions, the Climate FieldView platform gives farmers a deeper understanding of their fields so they can make more informed operating decisions to optimize yields, maximize efficiency and reduce risk. 

WDT has spent the past 17 years aggregating, cleaning, and providing analytics on weather data across the globe. This year, WDT has released high resolution data services for the agriculture industry using advanced data blending and modeling techniques, providing agriculture customers with gold standard weather data in global agricultural markets for their precision agriculture applications.

DTN’s Ag Weather Network has now grown to over 6,000 stations in just 4 years.  This growth has been driven by the need for precision local weather information in precision agriculture applications.  The network and the data it collects will be described, along with some of the applications and benefits to producers and other users of the information.