CropProphet Argentina Corn Yield Forecast
CropProphet is pleased to announce the release of Argentina corn yield forecasts starting in December 2020. We are also releasing Argentina soybean yield forecasts. The forecasts are created using a similar weather-based yield forecasting technology that we use for our US yield forecasts. Argentina is a significant producer of corn globally. The impact of weather on Argentina grain production impacts global grain trading markets because of its impact on global grain supply. In addition, a La Niña event is developing, which directly impacts Argentinian crops. CropProphet’s corn yield forecast enables grain traders to create opportunities and manage risks that may arise from not having a full view of global grain supplies.
CropProphet quantifies the impact of weather on crops by combining historical weather data with crop yield and production data and apply machine learning techniques. The result of the machine learning is a skillful model to predict Argentinian corn yield based on evolving weather conditions during the crop season.
Corn Yield Forecast Evolution
The monthly evolution (see animation below) of the 2019 forecast shows how the forecast evolved during the crop season.
Summary Information: Argentina Corn Yield Forecasts
Crops and Regions Covered
Daily-updated yield forecasts are provided for
- soybeans and
- corn.
The forecasts provided are at the departmental level (the small geographical regions below) but are also calculated for the provincial and national geographic level. The national soybean production map below highlights the regions of Argentina that produce the most corn.
Forecast Dates
The forecasts run from early December 2020 to June of 2021.
Historical Training Period
The model was trained using historical Argentina departmental estimated crop yields and departmental weather information from 1981 to 2000.
Accuracy: Argentina Corn Crop Yield Forecast
Our cross-validated, machine learning statistical modeling methodology enables evaluation of the accuracy of the CropProphet model. As shown in the graphic below, for Argentina’s national corn yield forecasts, CropProphet achieves a mean absolute error of 236.6 kg/ha over the 18-year period. Compared this to the total 2019 final corn production estimate of approximately 7700 kg/ha, this is an error of about 0.5%, on average.
We find the corn yield forecast accuracy achieved quite compelling. To understand and forecast US crop production, the USDA NASS provides data helpful for crop yield forecasting. This includes, for example, a long history of weekly crop condition reports. This data is not available from the Argentine Ministry of Agriculture, Livestock, and Fisheries making Argentina corn yield forecasting that more challenging.
As a result, we’ve studied the impact of weather conditions on Argentina’s end of season corn yield. The graphic below shows the correlation of different weather conditions (along the x-axis and anonymized, in this case) by date of the growing season (y-axis). A higher positive or negative correlation means that a specific variable has a more significant impact on the end of season yield. The graphic suggests the period from December to April of each year has important impacts on the end of season corn yield.
Argentina Corn Weather Forecast
The CropProphet Argentina corn yield forecast is a weather-driven model. As a result, we process a wide variety of weather and climate information required to create the corn yield forecast. This information includes weather forecast information from NOAA’s global ensemble forecast system and the European Centre for Medium-Range Forecasting ensemble model. Grain markets respond to daily changes in the weather forecast. Having access to the primary weather forecast tools is critical for trading success.
The weather observation and forecast information are available in CropProphet to help your monitoring of the weather conditions important to South American crop development.
Example Precipitation Forecast
Example 90-day Observed Precipitation Analysis
Estimating the impact of weather events on crops can be difficult. We simplify the process by calculating production weighted weather variables such as
- precipitation
- maximum temperature,
- minimum temperature,
- growing degree days,
- soil moisture,
- relative humidity, and
- solar radiation.
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