Agricultural dry lands constitute the single largest livelihoods resource base in the semi-arid regions. The semi-arids are defined as areas with adequate rain and soil moisture for 75-180 plant LGP (Length of Growing Period) days. The semi-arids form the single largest landmass in India. States, which contain significant areas of this include, amongst others, Rajasthan, Gujarat, Madhya Pradesh, Maharashtra, Karnataka, Andhra Pradesh and Tamil Nadu. The semi-arid region literally provides the collective Indian stomach: >65 % crop production in the country is rainfall dependent.

Stress to food security

• Decreasing biomass and bio-diversity
• Increasing rain delay/failure
• Decreasing soil fertility and crop productivity
• Usurious local interest rates of 36%-72%, and
• Low access to cheap institutional credit, compounded by a high incidence of defaulters.
  
  But more critically, we are in the process of losing the very ground on which our lives are built on:
• About 187 million hectares (ha) of land in India is already degraded.
• Each hectare loses slightly more than 1 mm of its top soil every year. This translates into a horrendous average national soil loss of 16.3 tonnes/ha/year!
• Soil erosion is estimated to cause an yield decline of 0.14 tonnes/ha/per mm of soil loss. (Resource Management in Rainfed Drylands - MYRADA and IIRR, 1997 Vision 2020, Div. Of NRM, ICAR)

Some ICT dream tools in agriculture
The following applications are based on remote sensing, GIS and on wireless telemetry for rainfall data. While remote sensing has existed for many years, it is only recently that satellite images with resolutions as high as 70cm (which allows any object that is 70 cm long to be seen with the naked eye) are now available. Cost implications of this technology have restricted the use of this technology primarily to macro planning. But it is in its micro use, that this technology comes into its own. While each of the following applications is also a stand-alone, it is in the convergence of remote sensing, telemetry and GIS that the real solutions for Indian agriculture will be found.

Rainwater harvesting: Technical guidelines for the construction of Farm Ponds ask for a catchment of 7 ha for a pond of 9mx9mx9m in areas with around 500mm of rain. A simple planning tool can be created with the use of 5.8m satellite images from the National Remote Sensing Agency. Updated drainage lines can be interpreted from this, and a watershed ridge line delineated. A minimal GIS tool can be created to generate 7 ha squares. And you have a dream tool (Fig-1) that can be used by farmers, line department officials and bankers to examine where farm ponds can be sited, and for drawing up departmental annual action plans. And this can be adapted with ease for different rainfall areas. Modelling tools also exist to translate the existing 20m contour height levels from the Survey of India topographical maps into 1m contour height levels.

The GIS tool can be modified to also include farm bunds to facilitate soil conservation plans, while both the bunds and the drainage lines can provide planning and monitoring data for vegetative treatments.

Understanding soil: Tonal signatures from a satellite image can be interpreted, ground truthed, and form the basis for a semi-detailed soil survey. With the help of a good soil scientist, this can provide a soil map with >60% accuracy at the individual plot level. With soil as the foundational data, a soil scientist can now interpret the Land Capability Classification (LCC) for a given area. Basically, this defines what soil is good for what land use and crop. The use of satellite imagery and a linear classification allows this process to create LCCs with finer gradations. With a plot’s LCC known, it is now easy to examine whether an existing land use is appropriate to the soil’s carrying capacity. Soil also provides a foundational tool to understand the vulnerability of a plot of land to soil and moisture erosion. By extrapolating plot area, soil type, slope and maximum rainfall over the past 25 years to a standard run-off formula, it is possible to define which plot is at very high, high, medium, low and no risk for erosion. Both carrying capacity analysis and lands at risk have been developed by SAMUHA’s Centre for Remote Sensed Micro Applications (CReSMA).

Food fodder fuel budgeting: This is a tool developed by the University of Agricultural Sciences, Bangalore, for ISRO’s Integrated Mission for Sustainable Development. Using this, satellite images can be interpreted for biomass and converted into a Supply figure. Similarly, population in a given area is then quantified as a demand figure. Based on this, an area can be assessed as being surplus or deficit. Given the criticality of fuel wood in rural areas, specific interventions can be undertaken to establish woodlots to meet a village’s fuel requirement, even as a village’s fodder requirement is assessed and specific steps taken to grow more biomass for livestock development.

Plot Map: Most revenue maps are a pictorial depiction of land ownership, with each survey number connoting an individual owner. Unfortunately, revenue maps in India are 30-60 years out of date. All changes in land ownership since the map was last created are now maintained in a khata (plot) register. Because there is no co-relation between the visual map and the text register, the village accountant becomes inordinately important. Satellite images, now available at 70cm resolution, provide a simple way of upgrading and updating all revenue maps in the country. SAMUHA has defined a plot as the unit for base maps. In GIS terms, a plot is a polygon, and when this is attached to a record, it allows the map to be queried for all the data contained in the record. By clubbing different factors, it is also possible to create a map showing household vulnerability. This household Vulnerability Index was generated using landholding, caste and gender as parameters.

Farmer card: SAMUHA believes that an overview of all the factors affecting their lands can help farmers make the transition from being cultivators to becoming managers of their resources. The Farmer Card provides information – ownership, location, drainage, soil, hydrogeomorphological, recommended cropping and conservation measures for each of their plots.

Credit inventory: SAMUHA presently uses the data generated from remote sensing in the planning and implementation of 7 watershed projects supported by the National Bank for Agriculture and Rural Development (NABARD) Watershed Development Fund, the Indo-Swiss Participatory Watershed Development-Karnataka project, and Plan International. The co-relation of plot-level Land Capability information to existing institutional credit and land development schemes led us to hypothesise that we could generate Rs 5 million in bank and departmental credit and schemes for every 1000 ha. This is now being tested in a 10,000 ha study area in partnership with HP Kuppam-i-Community in the Shantipuram Mandal in Chittoor district, Andhra Pradesh.


Wireless Telemetric Rain Gauges (WTRG): The reality of the semi-arids is that even after water, land development and credit have been addressed; agriculture here is still rain dependent. The Drought Monitoring Cell (DMC) is a little known scientific unit of the government of Karnataka. Despite a serious paucity of resources, DMC has done cutting edge work in developing the use of rainfall data into Taluk level Aridity Index Reports which provide a critical Drought Monitoring Information System to the government and to farmers.

While DMC has been able to get government of Karnataka to now order the establishment of rain gauge stations in every Gram Panchayat, its work is handicapped by the availability of real time data. This is now being addressed by a wireless telemetric rain gauge, which uses GSM technology to facilitate each WTRG to send a SMS message to DMC on how much rain has fallen in the previous 24 hours. DMC is also committed to redesigning its Taluk Aridity Index to a Gram Panchayat-level. This will ensure that for the first time, farmers will receive real time rainfall data.

Conclusion
At the end of the day, dryland agriculture is still the most sustainable of all our agricultures. Scarce natural resources led our farmers to make farming part of a life style. Much of the ills of agriculture have arisen from a consumer culture, which has taught us to live beyond our means. Technology presently provides the easiest means to continue and to increase the exploitation of our natural resources. And in the process, technology also takes a bad name. Especially amongst the marginalised and the disadvantaged who pay disproportionately for this misuse. The challenge for the future is not technological: it is how we can use the creativity inherent in technology for the greater good of all, not just some good for a few.