Q&A: Deep Dive Into Gro's Predictive Yield Models

What is a crop yield model? What models does Gro Intelligence currently produce? 

A crop yield model is a supply prediction tool updated throughout the year for forecasting end-of-season yield for crops, based on environmental conditions and crop-health information. 

Gro has a growing catalog of yield forecasts as seen below along with the year we launched them:

• US: Corn (2016), Soy (2018), Winter Wheat (2019)
• Argentina: Corn (2020), Soy (2020)
• Australia: Wheat (2021)
• Brazil: Corn (2020), Soy (2020)
• Canada: Spring Wheat (2020)
• China: Corn (2019), Wheat (2021)
• India: Wheat (2018)
• Russia: Winter Wheat (2019)
• Ukraine: Wheat (2019)

Gro’s models cover 90% of global soybean exports, 70% of global corn exports, and 50% of global wheat exports. 

Why are yield models important?

Yield is the most important supply-side driver of the price of a commodity, and yield forecasts drive changes in the futures price. Governments and agribusinesses rely on such models to help manage foreseeable imbalances between food supply and demand.  Financial traders buy or sell financial assets based on their expectations of changes in crop futures prices, and yield models are a significant input to their own predictive financial models. 

How are Gro’s yield models calculated? 

Our models begin with custom-built Gro crop masks, which are select land areas identified by satellite imagery that are designated for growing a specific crop.  Within these crop masks, Gro’s models ingest data on environmental conditions such as: rainfall, drought, vegetative health and density, soil moisture, land surface temperature, soil properties, and more. 

An important predictive input for yield is NDVI, or normalized difference vegetation index, which uses satellite imagery to measure the amount and vigor of vegetation on the land surface and helps to distinguish plant life from surrounding soil. It is considered to be among the strongest indicators of crop health and of the potential for above or below average yield. 

Gro combines the satellite data with ‘ground truths,’ which are reliable and respected sources with at least a 20 year track record. These sources are typically official government-produced information, such as the USDA’s National Agricultural Statistics Service (NASS), or Brazil’s IBGE, the Brazilian Institute of Geography and Statistics, for example.  

The ground truths include the history of a given region as well as neighboring regions, which allow Gro’s machine learning (ML) models to train the history for that time of year and any recorded impact on yield if and when those conditions changed. 

What makes Gro’s models unique? 

Daily updates

One of the most important differentiators of our model is that they update daily, whereas the next most reliable sources in the US are weekly crop conditions reports and monthly yield forecasts provided by the USDA. 

Daily updates not only allow customers to have the most up-to-date information throughout the season, it also reveals multi-day trends in daily weather conditions that could impact crop health. The path of these trends is particularly useful to preempt the forecast changes that one might expect from government reports in the following week or month.  

Outside of Gro, customers often have zero visibility into daily changes in environmental conditions and crop conditions. The futures market reacts strongly to updates given monthly by the USDA because that has historically been the most timely and trusted update available. Severe weather, however, can have a significant impact on conditions in much narrower timeframes.  A multi-day trend in extreme high or low precipitation and temperature can affect crop health and precede serious reactions in the crop futures markets. Alternatively, daily updates can also reveal when multi-trends are potentially reversing.  With this capability, Gro’s models allow agribusiness, financial traders, insurance negotiators, procurement officers, and more, to get ahead of forthcoming market-moving news out of the USDA and equivalent institutions globally. 

Global coverage:

In countries that have strong data reporting and analysis, Gro is able to produce highly accurate forecasts months ahead of final government reporting. Gro’s US yield model forecasts for final yield, for example, are released up to 4 months in advance of USDA’s January report, with 98% accuracy.  

In other parts of the world, however, data accessibility and validation can be extremely limited. However, thanks to powerful machine learning capabilities and strategic ground truth partnerships, Gro’s final yield models of these crop growing regions are still 95-98% accurate, and are available up to 10 months in advance of final government reports. 

Regional granularity: 

Beyond the obvious benefits of accurate predictions for national crop yields and futures prices, several commercial actors such as input companies, crop insurance companies, procurement professionals and food and beverage companies all seek to understand sub-national (county or district) level yield data. 

Gro’s yield models operate at the county or district level, can compare conditions between neighboring regions, and can also detect anomalies in yield forecasts within a given region from its 5 year average. 

What are some examples of how customers use these models? 

Commodity traders in the US are able to use Gro’s daily-updating yield models to get ahead of the weekly crop condition reports, and monthly USDA end-of-season forecasts, which typically influence the futures prices of agricultural commodities.  

Food and beverage company managers, procurement officers as well as insurance planners can utilize our yield models to identify threats to supply and demand and protect their business models against potential changes in commodity prices.  They can compare data to the same region and time of year as years past to understand if trends are within a normal range of volatility, whether new records are being broken and/or whether permanent trends are emerging. 

How does Gro improve their models over time? 

Our models are engineered to calculate a set of variables that each have an assigned weight, depending on how much it typically influences end-of-season yield. These variables change in relative importance over the season depending on geography, weather conditions, historical data patterns, availability and recency of both ground truth and satellite data, and more. Each day, our yield models recalculate the forecast based on the best currently available sources of data and Gro is continuously studying new sources that could improve accuracy and timeliness. 

Is there anything else to note about Gro’s yield forecasting process? 

Gro’s yield forecast models remain highly accurate and available months ahead of official government reporting. We have over 2 million unique, proprietary data series that address a range of questions across crop yield and production, supply and demand, growing conditions, and climate scenarios. Gro’s data series are built by our team of 50+ domain experts and scaled through artificial intelligence, generating predictive models and real-time applications. 

Please read our 2022 Performance Report for more detailed information.  

To schedule a demo of our yield models or other products, click here.