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Discussion In recent years, remote sensing has been accepted as an indispensable tool in the field of agriculture. It helps in crop acreage and yield estimation; health monitoring, agriculture information system, supply chain management, etc. by trimming the cost and human effort. It is increasingly being used to take vital decisions for crop marketing and export, crop inventory and commodity trading. The major activities in SCM that are answered through this RS & GIS approach are:

  • 1.

    Satellite based crop mapping and acreage estimation at district/tehsil level

  • 2.

    Crop health monitoring using temporal satellite images and derived vegetation indices

  • 3.

    Satellite derived indices and weather parameter based yield estimation

  • 4.

    Comparative analysis on remote sensing based production and actual arrival in the market

  • 5.

    Spatial database creation of agri-market location and proximity analysis from the produce

  • 6.

    Vehicle routing from farm to retail outlet using network analyses

  • 7.

    Decision support system for supply-chain design and management It is evident from here that AI data produced using geospatial technology

is authentic. However, the data in raw format may not yield results. Hence a methodology is discussed to use the above data for supply chain management. This approach uses a statistical model developed to use the AI data for making price forecast of different Basmati varieties, a robust statistical model was used as given in the following equation:

Name of the Mandi

Amritsar & Tarn Taran Mandi, Punjab

Basmati Varieties

Sources of Rice Arrived in

Mandi (in %) Material

(District)

Basmati 386 Basmati 370 HBC 19 Sharbati Pusa 1121

Super Basmati

Amritsar

60-70% 60-70% 20-30% 50-60% 60-65% 50-60%

PB 1

Patiala

15-20%*

Super

15-20%*

Basmati

20-25%*

Sharbati Basmati 386

30-35%*

Basmati 386

30-35%*

Hoshiarpur Pusa 1121

Basmati 386 Ludhiana

10-15%* 25-30%*

Gurdaspur

20-25%*

15-20%*

Kaithal

15-20%*

Sirsa

10-15%*

Sharb PB 1 PB 1 PB 1

ati

Equation 1

Where,

Price = {ƒ(ma, ta, cd, pv)}/ƒprice

Basmati 386

Saharanpur 20-25%*

PB1

8-10%*

  • *

    Depend on Mandi rate

ma

  • Mandi Arrival

able-1: Gives details of mandi data collected from a sample mandi survey in Amritsar and aran mandi in Punjab.

Total Basmati Arrival in Mandis [b]

49.16

63.36

68.97

65.45

Personal Use and Seed Purpose [c]

15.00

10.00

10.00

10.00

Contract Farming

6.67

1.00

2.00

2.00

Sold through other channels d]

Millers Agents

6.67 6.67

2.00 7.00

2.00 1.00

2.00 1.00

Unorganised Sector

10.00

10.00

10.00

10.00

Sub-Total [e] = [c +d]

45.01

30.00

25.00

25.00

Total Basmati Accounted by Satellite Based Study [f] =[b +e]

94.17

93.36

93.97

90.45

ƒprice = ƒ(last year low, last year high, Month, Mandi arrival)

able-2: Gives the results so produced and comparison analysis

32

Conclusion Conventional methods of generating data such as undertaking extensive surveys are time consuming and expensive. At times the government takes important decisions on importing wheat, rubber, sugar, etc with insufficient and pseudo-geospatial data. Similarly, traders rely on the information provided by the midd lemen who in turn get this information from the farmers.

This methodology clearly depicts the advantage of Geospatial based supply chain management techniques. However, the study has a few limitations such as paucity of secondary data and cloud free satellite data especially during Kharif season. However, if the industrialists and federal government jointly produce such data, future generations will have easy utility of this supply chain management model.

i4d | January 2009

Punjab Haryana

Uttar

Uttaranchal

Pradesh

100.00 100.00

100.00

100.00

ta cd pv

  • Time of Arrival

  • Competitor details

  • Production of variety

Comparison of Kharif 2005-06 Satellite-based estimated production and Mandi arrival for Punjab, Haryana, UP and Uttaranchal

Description

Satellite Based-Estimated

Production of Basmati 2005 [a]

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