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Examining relationship between

NDVI (Normally Differentiated Vegetation Index)

and

production/procurementusing freely available Remote Sensing Data

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Motivation I

NDVI (Normally Differentiated Vegetation Index) has been used

extensively to measure vegetation cover characteristics, cropassessment studies, monitor health of crops over large regions,monitor vegetation change and estimate biomass. Time seriesanalysis of NDVI allows establishment of a baseline for normalvegetation productivity for a region.

Moreover, it has been used in regression models to predict crop

harvests with high degree of accuracy. Crop residues/green fodder is universally used as the primary bulk

feed either in-situ or ex-situ. Crop residues are stored typically for 6months to one year, after the harvests.

production is dependant upon the availability of feed.

procurement trend would follow that of production if all external

conditions remain same including competition and payment tofarmers.

Thus, procurement or production may be linked or correlated withNDVI with a lag of 6 months

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Motivation II

Normally remote sensing data is expensive and its analysis requires

expert manpower and costly software. However, GLAM (Global Agricultural Monitoring project) of USDA

has been providing processed NDVI data for all parts of India at a

comparatively high level resolution of 250 sq.meters. This is

provided through internet free of cost and updated every fortnight.

Village boundary information can be now overlaid with NDVI data(downloaded from USDA) to arrive at the composite NDVI for any

specific village for every fortnight.

Thus, it is possible to correlate this data with monthly time series

milk procurement data for the villages. Thereafter, suitable models

can be developed for predicting short term changes in

production/procurement.

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Experimental work with 2 villages (Waghod &Morgaon ) in Jalgaon

based upon procurement data between Apr-Nov05 & Apr-Nov06

Downloaded NDVI data for India

and specific area in Jalgaon district

Source: Global Agricultural Monitoring Project, USDA

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Example: NDVI information during the fortnight Sep 13 Sep28, 2004 downloaded from

USDA website and overlaid on village boundary information of Jalgaon District. The areaunder study highlighted in box showing the 2 villages.

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Combined monthwise procurement in 2005-06 and 2006-07in the 2 villages (In Kgpd)

0

100

200300

400

500

600

700

800

900

1000

APR MAY JUN JUL AUG SEP OCT NOV

PROC0506

PROC0607

PROC0506 PROC0607

APR 680 617

MAY 501 456

JUN 446 318

JUL 485 295

AUG 483 250

SEP 575 329

OCT 774 394

NOV 895 568

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NDVI between harvest to next harvest 2004-05, 2005-06 & 2006-07 in

the specific area along with the short-term mean of 5 years

NDVI is at its peak value just before harvesting. NDVI has been found to have very strong correlation

with biomass availability (Kg/sq.m). We may see that 2004-05 had been a good crop year in comparison

to 2005-06.

70

72

74

76

78

8082

84

86

88

90

SEP OCT NOV DEC JAN FEB MAR

NDVI0405

NDVI0506

NDVI0607

NDVI0405 NDVI0506 NDVI0607

SEP 88 87 82

OCT 85 83 82

NOV 84 80 81

DEC 83 79 81

JAN 83 78 80

FEB 83 76 79

MAR 75 75 72

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Correlating lean-flush monthly NDVI values with procurement with a

lag of 6 months beginning with harvesting month i.e. NDVI values

between Sep-Feb of the previous year vs. procurement in Apr-Sep in

current year

PROC0506 PROC0607

PR 680 617

MAY 501 456

UN 446 318

UL 485 295

UG 483 250

SEP 575 329

OCT 774 394

NOV 895 568

NDVI0405 NDVI0506

SEP 88 87

OCT 85 83

NOV 84 80

DEC 83 79

AN 83 78

FEB 83 76

MAR 75 75

CORRELATION 0.71 0.90

We observe a high degree of correlation in both

the years.

This may be explained by the fact that in 2004-05

the general harvest in Sep04 was much betteras evidenced by high NDVI values, which

reflected in higher production/procurement in the

forthcoming lean in Apr05.

In Sep05 the harvest seems to be poorer with

lower NDVI values and corresponding drop in

production/procurement in coming lean seasonbeginning Apr06 onwards.