Agriculture Information Service Built on Geospatial Data Infrastructure and Crop Modeling

Honda, Kiyoshi; Ines, Amor Valeriano M.; Yui, Akihiro; Witayangkurn, Apichon; Teeravech, Kumpee; Chinnachodteeranun, Rassarin

An agricultural information service platform, called FieldTouch,
is being built and tested on geospatial data infrastructure and crop
modeling framework. More than 100 farmers in Hokkaido, Japan,
have been participating on this development and are utilizing the
services for optimizing their daily agricultural practices, e.g.,
planning and targeting areas where to apply fertilizer more to
enhance homogeneity of growth and robustness of crops in their
FieldTouch integrates multi-scale sensor data for field monitoring,
provides functionality for recording agricultural practices, then
supports farmers in decision making e.g., fertilizer management.
RapidEye satellite images are being used for monitoring
vegetation status updated every two weeks. Field sensor data from
25 nodes record soil moisture and temperature data at different
soil depths, and suites of meteorological variables e.g., rainfall,
minimum and maximum temperature, solar radiation, wind, etc.
every 10 minutes. Data from national weather observation
network, AMeDAS, is also a source of daily weather data. We
used "cloudSense" sensor backend service that serves meta-data
and data to FieldTouch via a standard web service called SOS
(Sensor Observation Service), which brought great flexibility and
enhanced automation of system’s operation.
Using agronomic data from experimental station, the cultivar
parameters (genetic coefficients) of a local wheat variety were
calibrated for the DSSAT (Decision Support System for
Agrotechnology Transfer) crop model using data assimilation.
These were built in a web-based DSSAT wheat crop model called
Tomorrow’s Wheat (TMW) where in a user can explore the
effects of timing of sowing at a given climatic condition, soil and
crop management. TMW accesses long-term weather data from
the on-line observation station up to the most recent archive,
parameterize a built-in weather generator, then generate 100
weather scenarios then runs the wheat model at the chosen
planting date, then two weeks, and one week before and after that.
The yields are presented as distribution of yields at these different
planting options. Future developments are going-on to personalize
more the system so that the user can input fertilizer scenario, and
be able also to apply seasonal climate forecast, and link to the 25
sensor nodes to simulate current plant conditions given a
management scenario. In this way, the user can be informed better
on how to manage their sources of vulnerabilities in their fields.


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Also Published In

Proceedings of the 2014 International Workshop on Web Intelligence and Smart Sensing

More About This Work

Academic Units
International Research Institute for Climate and Society
Association for Computing Machinery
Published Here
October 3, 2014