Welcome to this talk on GIS Applications in
Agriculture. This is Part 2 of the talk and
this focuses on applications of GIS.
My name is Balaji. This talk is presented
in collaboration with Mr. G Sreedhar who is
a doctoral scholar at IIT-Bombay in India.
In Part 1, we looked at what is GIS? We looked
at GIS as essentially a data management activity
with spatial as well as non-spatial data.
Now what is spatial data? We said it includes
location, time and attributes such as soil
moisture, air temperature and so on. And there
were two formats: raster and vector for all
spatial data. They were interconvertible.
They could be converted from one to another.
There were three types of data namely point,
line and polygon. These are also interconvertible.
In effect, GIS processes include the following:
one is data inputs. It includes spatial and
non-spatial data, which we just described.
Data manipulation - point to line, line to
polygon conversions, vector to raster conversions
etc. Management includes adding, editing,
joining and relating tables with which you
are familiar when you were taught data management.
Query and analysis. So, for example, you want
to find a flood affected village or drought
affected village or a village otherwise affected
by some disaster. You can do query of -- query
of -- query and determine that particular
village. You can do analysis in a similar
way.
Visualization includes color coding, graphs,
embedded photos etc. Some of these would be
non-spatial data and we give you an exercise
in Part 1 to look at Google Maps where you
would have understood how non-spatial data
is routinely brought into as part of visualization.
Applications of GIS in Agriculture are actually
quite a few. I mean, an important area is
yield monitoring. The other area is soil fertility,
pest mapping. There are new areas emerging
which relate to crop health assessment. GPS,
you know, Geographic Positioning System, which
is a very very important part of GIS applications
in agriculture, it's highly useful in promoting
precision farming and geo-database creation
is also an important activity. So you should
look at the fact that GIS when it comes to
applications in agriculture, it is truly multi-dimensional.
Now let's take an example. Here is a locust
forecast provided by FAO, the Food and Agriculture
Organization of the United States of which
practically every country on Earth is a member.
The FAO provided this forecast as you can
see in March 2015 because locust is a major
pest, causes immense damages to crops in many
countries and locust forecasting has been
a major activity of FAO.
What this map shows, I mean, besides the color
codes is a huge region ranging from Atlantic
coast all the way to the Bay of Bengal and
Indian Ocean. It's a huge region covering
many, many countries and billions of people
and it's in a single map they are able to
provide some kind of assessment of perceived
risk or threat of current desert locust infestations.
And here in this map, the same set of data
is presented for a smaller region and you
find that as the region becomes smaller, finer
and finer data becomes finer and finer representations
are possible. Now you can see, for example,
in this map swamps, bands, groups, adults
etc., which you could not easily represent
in a region in a much larger regional map
and that brings us to one very, very important
lesson, the Micro-Regional Monitoring.
We are dealing with developing countries and
developing countries, the most decisions are
made by small farmers when it comes to production.
They make it either in conjunction with government
inputs or with other similar institutional
inputs, but they by and large make it in a
micro-regional setting, micro-regional context.
Therefore, providing information in the form
of micro-regional GIS outputs is important.
And here is, for example, my colleague Mr.
Sreedhar and I have worked on this idea of
River Basin covering 594 villages in south
central India. In this you are able to see
through the legend a variety of water bodies.
You can also see a variety of crop types and
a variety of irrigation types. All that is
available only in a map which is operating
on this scale. This is why micro-regional
monitor -- micro-regional level work in GIS
is very, very important if you want to make
sense to very large number of small farmers.
And modeling of soil for crop field, for example,
is a major, major application of GIS. For
example, you can use GIS to determine how
a soil type, fertilizer application and water
are tend to affect crop field. You can see
multiple layers in this particular system
and this is slowly becoming a major activity,
namely soil fertility mapping and nutrient
management. This is -- this can become an
important solution for crop yield database.
As more and more countries are worried about
the increasing costs of fertilizers as inputs
into agriculture, tools like this are getting
more and more important.
The other is soil mapping and sampling. Again,
my colleague Sreedhar and I have worked on
a very, very micro level to arrive at this
kind of a map. You can see again we have covered
just a few hundred villages and you can see
very wide variety of soil types here which
allows a local decision maker or even a farming
-- farmers association to arrive at some sensible
conclusions on their own.
The other area is for drought monitoring because
climate change is increasingly accepted as
a major risk and drought is a major, major
phenomenon that affects millions and millions
of people and it's now seen to be an integral
aspect of climate change. We should be able
to use GIS to help people understand how vulnerable
they are to drought in a particular season.
And here is one effort and the area has been
divided into watersheds and watersheds have
been identified on the basis of their vulnerability
and not just vulnerability, this is based
on surface water availability, which in turn
is based on rainfall. All these can now be
put to a very good -- GIS can be put to very
good use in representing all these things
to policymakers whether at the local level
or at a higher level.
Now what are the important GIS software? There
are two classes of software. There is commercial
proprietary ones and there are open source
ones. In commercial proprietary ones, ArcGIS
is a very popular one. Expensive. It's more
for -- it is used for vector, vector datasets.
ERDAS IMAGINE is for raster datasets. IDRISI
can work with both and IDRISI is considered
to be more affordable. It's used in many developing
countries.
In terms of open source, ILWIS at ITC, Netherlands
is a free source and it has been available
since 2012 in a big way. Quantum GIS, which
is now called QGIS is an Open Source software,
which has enormous potential for use in agriculture.
And I also want to mention Elshayal, which
is a desktop client recently developed in
Egypt in the Arab world to work with Google
Maps and I believe this has also a great deal
of potential in agriculture.
Now what are the sources of satellite data?
Landsat is the one of the most important sources
owned by US government, US public and it's
operated by US Geological Survey and NASA.
And as you can see here, their maps are of
very, very -- their outputs are of very, very
high quality. It shows how in a short time
of no more than seven weeks you find how a
lake is drying up due to drought in a particular
part in the United States.
And among developing countries Bhuvan, which
is an Indian product, it's also available.
It focuses much more on India and this is
also available. A wide variety of datasets
are available for free download. This also
something some of you can make use of for
building your own outputs.
Now GIS-based decision support systems in
agriculture are becoming increasingly important.
My colleagues Mr. late Dr. Reddy and Dr. N
H Rao have been very active in this area.
And as you can see in the left side they have
identified digital maps as well as input attribute
databases and the type of decisions they can
support. They've worked, of course, more at
a regional level, but I believe this can be
scaled to work at micro-regional level as
well.
Precision farming is going to be a key application
of geo-spatial technology and at this time,
it is capital intensive. It requires a lot
of inputs that come from capital intensive
processes and from high technology applications,
but we should be able to envision precision
farming for small farmers at which time many
of you who are doing this course should be
experts in GIS applications in agriculture.
So now I thought I can conclude this part
too with offering you some ideas on exercises.
You should go to this FreeSmartGIS blog and
download and install Elshayal.
And remember that whenever -- irrespective
of the software, whenever you download and
use software, there are always risks involved.
Please understand them before you do so and
after you download, follow the instructions
on the blog and Google Map -- and use Google
Map imagery. Trace a stream or another water
body in your locality on the downloaded map.
Now just look at the possibility of doing
this in a smartphone as well.
This is an open-ended exercise. It carries
no grade, but I thought you could try this
and it will probably give you an idea of what
GIS can offer as a potential support for agriculture
for small farmers.
Thank you.
