The Gro US Farmer Profitability & Crop Budgets Application provides detailed, holistic views into US farmer profit and loss, combining disparate sources with proprietary Gro models across price, yield, area (planted and harvested), supply and demand, trade, and climate for key crops. This Application generates unique, highly accurate forecasts to improve and expedite in-season product pricing and distribution strategies, supply chain optimization, trading decisions, and more.
Asset and Regional Coverage
This Application provides insight into US farmer profit and loss both nationally and across 12 key growing states for corn and soybeans. It also covers US production, usage, and prices for corn, soybeans, wheat, sorghum, alfalfa, spring canola, and cotton at the national, state, and district levels. The Application incorporates Gro’s US Yield Forecast Models to provide daily-updating, in-season yield estimates for corn, soybeans, and wheat, a key indicator of crop supply and price. Visibility into global yield projections for corn, soy, and wheat – which can impact US net farmer profits – is provided for major producing countries at the national and state levels.
Users can also access Gro’s state-level data on climate and weather conditions, weighted by the crop of choice; Gro’s US planted acreage forecasts for corn, soybeans, wheat, and cotton; and fertilizer prices for eight different chemical types.
The Application offers an interactive crop budget tool that details the cost and revenue (both historical and projected) associated with growing corn or soybeans in a given region both nationally or across the 12 major growing states. The tool combines static historical data from top universities with up-to-date cash prices and daily-updating, in-season Gro US Yield Forecast Models, and enables users to enter their own data to explore and analyze various scenarios.
Types of Data Available Within the App
Gro Intelligence aggregates and organizes public data across a variety of sources and generates unique data series using proprietary models, such as: