Variability in Length of Crop Growing Period Causing Agricultural Vulnerability in India


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Authors

  • Kaushalya Ramachandran ICAR-Central Research Institute for Dryland Agriculture, Hyderabad 500 059, India
  • Shubhasmita S ICAR-Central Research Institute for Dryland Agriculture, Hyderabad 500 059, India
  • A Haritha ICAR-Central Research Institute for Dryland Agriculture, Hyderabad 500 059, India

Abstract

Satellite-data based Normalized Difference Vegetation Index (NDVI) can
indicate state of agriculture, crop vigour and hence can also be used to assess agricultural
vulnerability. Analysis of trends in NDVI for a given region can indicate the factors
that cause variability and the drivers that impart vulnerability to agriculture. One
such driver is variability in Length-of-Crop-Growing – Period (LGP). A methodology
was developed to determine LGP from time-series NDVI datasets from Start-of-Season
or greening-up phase to End-of-Season or drying-up phase. Public domain data like
NOAA-AVHRR based GIMMS data product (1982-2006) and MODIS-TERRA data (2000-
2013) were used to analyse variations in LGP contributing to agricultural vulnerability
in various agro-ecological sub-regions (AESR) in Peninsular India. Study carried out
at ICAR-CRIDA under NICRA indicates the variable trends in LGP and its impact on
agricultural production leading to agricultural vulnerability in India.
Key words: LGP, NDVI, agricultural vulnerability, crop phenology, AESR, rainfed
agriculture.

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References

AICRPAM-CRIDA 2011: Weather cock @ V 1.0.

NICRA.

Bhuvan: http://bhuvan.nrsc.gov.in/bhuvan_links.

php

Dadhwal, V.K. 2011. Retrieval of biophysical

parameters from satellite data. In Agricultural

drought: Climate change and rainfed agriculture

(Eds. Rao, V.U.M., Rao, A.V.M.S., Kumar, P.V.,

Desai, S., Saikia, U.S., Srivastava, N.N. and B.

Venkateswarlu). Lectures notes of the 5th SERC

School, CRIDA 52-58.

Defries, R.S., Hansen, M.C. and Townshend, J.R.G.

Global continuous fields of vegetation

characteristics: A linear mixture model applied

to multi-year 8 km AVHRR data. International

Journal of Remote Sensing, 21(6&7): 1389-1414.

Friedl, M.A., McIver, D.K., Hodges, J.C.F., Zhang,

X.Y., Muchoney, D., Strahler, A.H., Woodcock,

C.E., Gopal, S., Schneider, A., Cooper, A.,

Baccini, A., Gao, F. and Schaaf, C. 2002: Global

land cover mapping from MODIS: Algorithms

and early results. Remote Sensing of Environment

: 287-302.

Heumann, B.W., Seaquist, J.W., Eklundh, L. and

Jonsson, P. 2007. AVHRR derived phenological

change in the Sahel and South Africa(1982-2005).

Remote Sensing of Environment 108: 385-392.

ICAR-CRIDA 2014. Crop Contingency Plans Report:

http://www.crida.in:82/contingencyplanning/

ICAR-CRIDA 2017. District Database of Agricultural

Statistics: http://crida.in:82/ddas/

Jain, S.K., Keshri, R., Goswami, A., Sarkar, A. and

Chaudhry, A. 2009. Identification of droughtvulnerable

areas using NOAA AVHRR data.

International Journal of Remote Sensing 30(9-10):

-2668.

Kaushalya Ramachandran, Gayatri, M., Satish, J. and

Thilagavathi, N. 2013a. Monitoring Agricultural

vulnerability using NDVI time series.http://

www.geospatialworld. net/Paper/Application/

ArticleView

Kaushalya Ramachandran, Venkateshwarlu, B.,

Ramarao, C.A., Rao, V.U.M., Raju, B.M.K.,

Rao, A.V.M.S., Saikia, U.S., Thilagavathi, N.,

Gayatri, M. and Satish, J. 2013b. Assessment of

AGRICULTURAL VULNERABILITY IN INDIA 161

Vulnerability of Indian Agriculture to rainfall

variability - Use of NOAA-AVHRR (8 km) and

MODIS (250m) Time-Series NDVI Product.

Climate Change & Environmental Sustainability

(1): 37-52.

Kaushalya Ramachandran, Gayatri, M., Praveen, V.

and Satish, J. 2014a. Use of NDVI variations to

analyse the length of growing period in Andhra

Pradesh. Journal of Agrometeorology 16(1): 112-115

< http:// modis.gsfc.nasa. gov/sci_team/pubs/

abstract.php?id=09936>

Kaushalya Ramachandran, Praveen V. and S.

Shubhasmita 2014b. Assessing agricultural

vulnerability in India using NDVI data products.

International Archives of the Photogrammetry,

Remote Sensing and Spatial Information

Sciences 40(8): 39-46

abs/2014ISPAr.XL8…39K>

Kaushalya Ramachandran, Shubhasmita S., Praveen

V., Kalaiselvi, B. and Satish, J.2014c. Use of NDVI

to assess variability in length-of-crop-growingperiod

inducing agricultural vulnerability - A

study of Telangana Region in Peninsular India.

Global Environmental Research. Association of

International Research Initiatives for Environmental

Studies 18(2): 161-170.

Kaushalya Ramachandran, Rama Rao, C.A., Raju,

B.M.K., Rao, V.U.M., Subba Rao, A.V.M., Rao,

K.V., Ramana, D.B.V., Nagasree, K., Ravi

Shankar, K., Maheswari, M., Srinivas Rao, Ch.,

Venkateswarlu, B. and Sikka, A.K. 2015. Spatial

Vulnerability Assessment Using Satellite based

NDVI for Rainfed Agriculture in India. Central

Research Institute for Dryland Agriculture,

Hyderabad ISBN: 978-93-80883-35-9. 192 p.

Krishnaswamy, J., Kiran, M.C. and Ganeshiah, K.N.

Tree model based eco-climatic vegetation

classification and fuzzy mapping in diverse

tropical deciduous ecosystems using multiseason

NDVI. International Journal of Remote

Sensing 25(6): 1185-1205.

McKee, T.B., Doesken, N.J. and Kleist, J. 1993. The

relationship of drought frequency and duration

to time scales. 8th Conf. on Applied Climatology,

-22 January, Anaheim, CA, pp.179-184

Murthy, C.S., SeshaSai, M.V.R., Chandrsekar, K. and

Roy, P.S. 2008. Spatial and temporal responses

of different crop growing environments to

agricultural drought-A study in Haryana state,

India using NOAA-AVHRR data. International

Journal of Remote Sensing 30(11): 2897-2914.

Murthy C.S. and SeshaSai, M.V.R. 2011. Agricultural

drought monitoring and Assessment. In Remote

Sensing Applications (Eds. Roy, P.S., Dwivedi,

R.S. and Vijayan, D.) NRSC/ISRO, 303-330,

www.nrsc.gov.in

Nemani, R.R., Keeling, C.D., Hashimoto, H., Jolly,

W.M., Piper, S.C., Tucker, C.J., Myneni, R.B. and

Running, S.W. 2003. Climate-driven increases in

global terrestrial net primary production from

to 1999. Science 300: 1560-1563.

NRSC 2011. Land Use Land Cover Atlas of India

(Based on Multi-temporal satellite data of 2005-

. LUD-RS & GIS Applications Area-NRSC

(ISRO), Hyderabad, 128.

ORGCC (Office of the Registrar General & Census

Commissioner, India) 2011.Census of India-2011;

http://www.censusindia.gov.in/Census_

Data_2011/India_at_glance/popu1.aspx

SeshaSai, M.V.R., Ramana, K.V. and R. Hebbar

(a). Agriculture. In Remote Sensing

Applications (Eds. Roy, P.S., Dwivedi, R.S. and

Vijayan, D.) NRSC/ISRO, 1-20, www.nrsc.gov.in

SeshaSai, M.V.R., Murthy, C.S. and Ramana K.V.

(b). Agricultural drought assessment &

monitoring. In Agricultural drought: Climate

Change and rainfed agriculture (Eds. V.U.M. Rao,

A.V.M.S. Rao, P.V., Kumar, S., Desai, Saikia,

U.S., Srivastava, N.N. and B. Venkateswarlu),

Lecture notes of the 5th SERC school, CRIDA,

-87.

Thenkabail, P.S., Gamage, M.S.D.N. and Smakhtin,

V.U. 2004. The use of remote-sensing data

for drought assessment and monitoring in

Southwest Asia. Research Rpt. 85. Future

Harvest, IMWI, 25 p.

Thenkabail, P.S., Gangadhara Rao, P., Biggs, T.,

Krishna, M. and Turral, H.2007. Spectral

matching techniques to determine historical

land-use/Land-cover (LULC) and irrigated areas

using time-series AVHRR Pathfinder Datasets

in Krishna River basin, India. Photogrammetry

Engineering and Remote Sensing 73(9): 1029-1040.

Tucker, C.J., Townshend, J.R.G. and Goff, T.E. 1985.

African land covers classification using satellite

data. Science 227: 369-375.

Vrieling, A., Beurs, K.M.D. and Brown M.E. 2008.

Recent trends in agricultural production of

Africa based on AVHRR NDVI time series.

Remote Sensing for Agriculture, Ecosystems and

Hydrology X, Proc. of SPIE Vol. 7104 71040R-

-10.

Velayutham, M., Mandal, D.K., Champa Mandal

and Sehgal, J. 1999. Agro Ecological Sub Regios of

India for Planning and Development. NBSS&LUP,

Nagpur, India. Pub., 372 p.

White, M.A., Thornton, P.E. and Running S.W. 1997.

A continental penology model for monitoring

vegetation responses to inter-annual climatic

variability. Global Biochemical Cycles 11(2): 217-

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Submitted

27-12-2018

Published

04-04-2019

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How to Cite

Ramachandran, K., S, S., & Haritha, A. (2019). Variability in Length of Crop Growing Period Causing Agricultural Vulnerability in India. Annals of Arid Zone, 57(3 &amp; 4). https://epubs.icar.org.in/index.php/AAZ/article/view/85772