  Yield forecast is essential to agriculture stakeholders and can be obtained
with the use of machine learning models and data coming from multiple sources.
Most solutions for yield forecast rely on NDVI (Normalized Difference
Vegetation Index) data, which is time-consuming to be acquired and processed.
To bring scalability for yield forecast, in the present paper we describe a
system that incorporates satellite-derived precipitation and soil properties
datasets, seasonal climate forecasting data from physical models and other
sources to produce a pre-season prediction of soybean/maize yield---with no
need of NDVI data. This system provides significantly useful results by the
exempting the need for high-resolution remote-sensing data and allowing farmers
to prepare for adverse climate influence on the crop cycle. In our studies, we
forecast the soybean and maize yields for Brazil and USA, which corresponded to
44% of the world's grain production in 2016. Results show the error metrics for
soybean and maize yield forecasts are comparable to similar systems that only
provide yield forecast information in the first weeks to months of the crop
cycle.
