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Remote sensing Indian agriculture


RMSI has established a geospatial approach to rationalise a competent methodology for Supply Chain Management which can combat food insecurity

Dr K S Siva Subramanian Assistant Vice President Agriculture and Natural Resources, RMSI siva.subramanian@rmsi.com

ogesh Singh Project Lead - GIS, RMSI yogesh.singh@rmsi.com

Subrato Paul Project Manager Agriculture and Natural Resources, RMSI subrato.paul@rmsi.com


Subrato Paul

Introduction In tropical countries like India with 127 different agro-climatic zones, the impact of global climate change is evidential through varied seasonal variances such as droughts in Andhra Pradesh, Orissa, Tamil Nadu and flooding in places like Assam, Bihar, Orissa, West Bengal, Uttar Pradesh, Andhra Pradesh, etc. Coincidentally, these are also the major agricultural states. Furthermore, the dominance of middlemen increases the extent of food insecurity. The end result is that the government has to import foodgrains from other countries.

In India, we still predominantly use traditional techniques such as field based crop cutting experiments (CCE) to assess the crop yield and acreage. It is worthwhile to note that in India all crop exports and import decisions are still based on historical production data (previous year’s production records), as against the growing international trend of basing these decisions on more scientific and accurate methods such as assessing the current year’s yield and acreage much in advance of the actual production by using remote sensing and GIS techniques. The ramifications of taking crucial export and import decisions based on historical data is that there could be a perceived shortage or surplus. To cite an example, during FY-07, there was a bumper rubber production in India, as compared to previous few years. Still the same was imported and the price of Indian rubber went down, all due to non availability of timely data.

Agricultural data is currently generated by multiple agencies in multifarious ways; both conventional field surveys based as well as advance information technology based. Some of the prominent agriculture data publishing programmes in India are: CAPE (Crop Acreage and Production Estimation),

FASAL (Forecasting Agricultural output using Space, Agrometeorological and Land based observations). Federal Agricultural department generates the data by field sampling surveys. Industrial houses send their own field team to assess the acreage and production data. Agencies like Agriwatch, CSE are also gathering Agricultural Intelligence (AI) data from multiple sources. However, a prudent examination reveals that all the above data varies drastically.

Aiming to resolve such issues of vagaries in the AI data, RMSI has established a geospatial approach to rationalise a competent methodology for SCM (Supply Chain Management) which can benefit farmers, traders, exporters, industrial, government, and federal agencies to combat the exports. The author, in this paper highlights this through a specific case study conducted by RMSI for estimating crop acreage estimation, crop yield estimation and production estimation for various rice exporters.

Utility of Basmati Agricultural Intelligence (AI) data RMSI understands that AI data generated is used in different ways:

  • 1.

    Industries - Use this data mainly for procurement and supply chain management

  • 2.

    Boards - Use this data to streamline supply chain as well as to fix the price in the market

  • 3.

    Insurance companies - Create an insurance product out of the agricultural yield data However, the conventional data does

not suffice for many of the users. They need agricultural data modeled in such as changes in the cropping pattern from the last year, comparative analysis of the last

i4d | January 2009

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