  Forecasting crop yields is important for food security, in particular to
predict where crop production is likely to drop. Climate records and
remotely-sensed data have become instrumental sources of data for crop yield
forecasting systems. Similarly, machine learning methods are increasingly used
to process big Earth observation data. However, access to data necessary to
train such algorithms is often limited in food-insecure countries. Here, we
evaluate the performance of machine learning algorithms and small data to
forecast yield on a monthly basis between the start and the end of the growing
season. To do so, we developed a robust and automated machine-learning pipeline
which selects the best features and model for prediction. Taking Algeria as
case study, we predicted national yields for barley, soft wheat and durum wheat
with an accuracy of 0.16-0.2 t/ha (13-14 % of mean yield) within the season.
The best machine-learning models always outperformed simple benchmark models.
This was confirmed in low-yielding years, which is particularly relevant for
early warning. Nonetheless, the differences in accuracy between machine
learning and benchmark models were not always of practical significance.
Besides, the benchmark models outperformed up to 60% of the machine learning
models that were tested, which stresses the importance of proper model
calibration and selection. For crop yield forecasting, like for many
application domains, machine learning has delivered significant improvement in
predictive power. Nonetheless, superiority over simple benchmarks is often
fully achieved after extensive calibration, especially when dealing with small
data.
