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year data vs. the current year, production pertaining to respective mandis, settlement packages for farmers without affecting the profit margin of the insurance companies, etc. To create such intelligent data, RMSI followed two different aspects, namely,

  • 1.

    Geospatial data validity of comparing Mandi data with remote sensing based outputs in rice production

  • 2.

    Supply chain management methodology evolved from the above survey

Study area The study area covers major rice growing districts from the Indian Ganges flood plains. This includes 13 districts each in Punjab and Haryana, 29 districts in Uttar Pradesh, 4 districts in Uttarakhand and 2 districts of Jammu & Kashmir. Geographicall , this spreads across 25° 83’ North to 33° 07’ North latitude and 73° 87’East to 81° 86’ East longitude covering an area of about 189,000 sq km.

Input data The study entailed collection, procurement and analysis of primary and secondary sources of information. The broad classification of agricultural acreage over the entire region was carried out using IRS P6 AWiFS satellite images with spatial resolution of 56m. Information, from regional to local, were extracted using medium and high resolution satellite images of IRS series 1C, 1D, P6 LISS III and LISS IV with spatial resolution of 23.5m and 5.6m, respectively. Secondary sources of information like Survey

Figure 1: The study area

of India Toposheet on 1:50,000 scale, district maps for the study area were used as reference maps. RMSI also collected primary information for ground truth, field validation, sample based farmer survey and Mandi data through field based-surveys in all the districts.

Secondary information was also collected from district agriculture and state agriculture boards/offices for reference.

Methodology Major components of compiling a methodology for SCM included Market survey and assessment and Agriculture and Land Resource Mapping. While market survey is done through direct field authenticated data, agriculture and land use map was envisaged through interpretation of remote sensing satellite images.

With a cursory look at mandi information, it is apparent that both quantity and expected time of arrival information are vital. RMSI selected a sample district mandi in each of the Basmati growing states. Mandi arrival data (quantity) and time of arrival was collected from important mandis and market board at block/tehsil level and mandi board at district and state level. It was observed that

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Rice and Bajra (Pearl Millets) crops come early in the market i.e., September– October and Basmati and Sugarcane varieties come later in November– December months. To envelop this variation in time, a thorough, periodic and regular survey was carried out. Table 1 gives details of the mandi data collected from a sample mandi survey in Amritsar and Taran mandi in Punjab. Figure 2: Spatial Distribution of Basmati varieties in Punjab

Details of crop varieties availability, quantity availability and the market share in total purchase of produced crop was collected from different mandi head offices. Agriculture crop mapping was carried out using strict scientifically programmed algorithms of supervised and unsupervised classification in image processing software.Training sets collected from the ground survey were used in this process to identify and delineate different crops. Figure 2 shows the Basmati crop variety map for selected districts of Punjab. Analysis was carried out using the information collected from remote sensing interpretations and mandi survey from various sources. Final production estimation was carried out using combined analysis of remote sensing outputs and mandi data.

Geo-spatial data validity In conventional ways, after the estimation, second level sample surveys are carried out (crop cutting experiments) or remote sensing-based ground verification is done. However, the data produced, often does not sync with the final output data produced from mandis or markets or final government figure. This, in turn, leads to the question of authenticity of data produced from remote sensing.

RMSI undertook a hybrid approach to compare and assess the accuracy of remote sensing-based production estimation of Basmati rice for kharif 2005 against the mandis arrivals and other related sources at the end of the Kharif season-2005. Mandi arrival data of rice and Basmati was collected for Haryana and Punjab, whereas in Uttar Pradesh and Uttaranchal only total rice data was available and the same was collected. The survey experts also collected data from rice mills, state marketing boards of respective states and the Agents (Arthias). Analysis was carried out based on information gathered from various sources like discussions with respective mandi officials, mandi agents (Arthias), and rice millers to arrive at the conclusion. Based on comparative analysis between Kharif 2005 estimates derived using remote sensing approach and Mandi arrivals (as well as allied sources) as on 31st March 2006 and considering certain calculated and logical assumptions and limitations, it is concluded that the remote sensing based estimated results for Kharif 2005 is matching up to an accuracy of 90% to 94% in the states of the study area. As a by-product, an interesting result on RS data being always higher than total supply chain sources confirms the reliability of this data. Table 2 gives the results so produced and the comparison analysis.


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