  Digital agriculture has the promise to transform agricultural throughput. It
can do this by applying data science and engineering for mapping input factors
to crop throughput, while bounding the available resources. In addition, as the
data volumes and varieties increase with the increase in sensor deployment in
agricultural fields, data engineering techniques will also be instrumental in
collection of distributed data as well as distributed processing of the data.
These have to be done such that the latency requirements of the end users and
applications are satisfied. Understanding how farm technology and big data can
improve farm productivity can significantly increase the world's food
production by 2050 in the face of constrained arable land and with the water
levels receding. While much has been written about digital agriculture's
potential, little is known about the economic costs and benefits of these
emergent systems. In particular, the on-farm decision making processes, both in
terms of adoption and optimal implementation, have not been adequately
addressed. For example, if some algorithm needs data from multiple data owners
to be pooled together, that raises the question of data ownership. This paper
is the first one to bring together the important questions that will guide the
end-to-end pipeline for the evolution of a new generation of digital
agricultural solutions, driving the next revolution in agriculture and
sustainability under one umbrella.
