  Modern agriculture faces grand challenges to meet increased demands for food,
fuel, feed, and fiber with population growth under the constraints of climate
change and dwindling natural resources. Data innovation is urgently required to
secure and improve the productivity, sustainability, and resilience of our
agroecosystems. As various sensors and Internet of Things (IoT) instrumentation
become more available, affordable, reliable, and stable, it has become possible
to conduct data collection, integration, and analysis at multiple temporal and
spatial scales, in real-time, and with high resolutions. At the same time, the
sheer amount of data poses a great challenge to data storage and analysis, and
the \textit{de facto} data management and analysis practices adopted by
scientists have become increasingly inefficient. Additionally, the data
generated from different disciplines, such as genomics, phenomics, environment,
agronomy, and socioeconomic, can be highly heterogeneous. That is, datasets
across disciplines often do not share the same ontology, modality, or format.
All of the above make it necessary to design a new data management
infrastructure that implements the principles of Findable, Accessible,
Interoperable, and Reusable (FAIR). In this paper, we propose Agriculture Data
Management and Analytics (ADMA), which satisfies the FAIR principles. Our new
data management infrastructure is intelligent by supporting semantic data
management across disciplines, interactive by providing various data
management/analysis portals such as web GUI, command line, and API, scalable by
utilizing the power of high-performance computing (HPC), extensible by allowing
users to load their own data analysis tools, trackable by keeping track of
different operations on each file, and open by using a rich set of mature open
source technologies.
