Introduction

Gro’s Yield Forecast Models use a suite of machine-learning models to estimate in-season yields at the district, province, and/or national levels on a daily basis. Our Argentina Soy Yield Forecast Model predicts district-level soybean yield, which we then aggregate to the national level.

Customers Use the Model to:

  • Follow yield changes for a key player in global soy prices, and the world’s largest producer of soybean meal and oil
  • Predict future trends such as crop yields and production, agricultural input demand, and significant weather patterns
  • Gauge crop availability and crop prices
  • Understand how weather impacts yields in microclimates
  • Inform other models focused on damage caused by pests and diseases
  • Monitor crop production, the most significant part of the balance sheet, by tracking its most variable component, which is in-season yield 

Why It Matters

Argentina is the third-largest producer of soybeans in the world, and the largest exporter of soybean meal and oil. The country produces over 40 million tonnes of soybeans each year, funneled into biofuels and livestock feed in markets across the globe. Annual exports of these products contribute over $14 billion to the country’s economy. Following developments in yield for this particular crop is essential given its impact on soybean futures prices, soy biofuel availability, and livestock producers that depend on the country’s crop. 

Methodology

We model district-level yields and aggregate those up to the national level, with district-level harvested areas as weights. The model uses the following variables:

  • Normalized difference vegetation index (NDVI) signals of soy fields in Argentina, based on Gro’s specific land cover maps. Satellite-derived NDVI is a powerful indicator of plant health that uses infrared light waves to detect positive signals and plant stressors. 
  • Yield, area and production history in Argentina
  • Evaporation difference from mean 
  • Land surface temperature 
  • Weather forecasts of air temperature and precipitation 
  • Soil characteristics 
  • Latitude and Longitude 

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