  Data sharing remains a major hindering factor when it comes to adopting
emerging AI technologies in general, but particularly in the agri-food sector.
Protectiveness of data is natural in this setting; data is a precious commodity
for data owners, which if used properly can provide them with useful insights
on operations and processes leading to a competitive advantage. Unfortunately,
novel AI technologies often require large amounts of training data in order to
perform well, something that in many scenarios is unrealistic. However, recent
machine learning advances, e.g. federated learning and privacy-preserving
technologies, can offer a solution to this issue via providing the
infrastructure and underpinning technologies needed to use data from various
sources to train models without ever sharing the raw data themselves. In this
paper, we propose a technical solution based on federated learning that uses
decentralized data, (i.e. data that are not exchanged or shared but remain with
the owners) to develop a cross-silo machine learning model that facilitates
data sharing across supply chains. We focus our data sharing proposition on
improving production optimization through soybean yield prediction, and provide
potential use-cases that such methods can assist in other problem settings. Our
results demonstrate that our approach not only performs better than each of the
models trained on an individual data source, but also that data sharing in the
agri-food sector can be enabled via alternatives to data exchange, whilst also
helping to adopt emerging machine learning technologies to boost productivity.
