161

Use of NDVI to Assess Variability inLength-of-Crop-Growing-Period Inducing Agricultural Vulnerability:
a Study of Telangana Region in Peninsular India
Kaushalya RAMACHANDRAN1*, Shubhasmita SAHANI1,
Praveen Kumar VADDI1, Kalaiselvi BHIMAN2 and Satish JANGAM1
1

Central Research Institute for Dryland Agriculture, International Conference on Arabidopsis ResearchSantoshnagar, Hyderabad, Telangana, 500059, India
2
National Bureau of Soil Survey and Land Use Planning
Nagpur-440033, Maharashtra, India*e-mail: kausalya@crida.in

Abstract
The satellite data-based Normalized Difference Vegetation Index (NDVI) was used to assess the state of
agriculture and crop vigour on a temporal basis to study agricultural vulnerability to climate change on aregional scale in a semi-arid red and black mixed soil region in Telangana, in the southern part of the Indian
peninsula often referred to as the Deccan Plateau extend across 11.48 million hectares (mha) with 6.98 mha
under rainfed agriculture. It provides a source of livelihood for 3.3 million farmers with small land holdings
and 4.3 million farm labour and their dependants. The annual rainfall ranges from 600–1100 mm, of which
71% is received during the southwest monsoon period. Rainfed agriculture is the major land-use activity andincreased climatic variability in recent decades has resulted in frequent losses, forcing governments to findsuitable solutions. In order to understand factors that contribute to increasing agricultural variability inTelengana, and to understand trends in climatic variability and extreme weather events and their impact on
agricultural production, time-series AVHRR NDVI data products were analysed and corroborated with theStandard Precipitation Index (SPI). The length of crop-growing period (LGP) was estimated from NDVI andstudied as a Sensitivity Indicator for agricultural vulnerability, as it indicates crop health and vigour, which
determine agricultural yield.
The results indicate which crops can be cultivated, which are vulnerable and the possible spatial extent ofagricultural vulnerability based on an analysis on a regional scale, viz., the agro-ecological sub-region(AESR) delineated on the basis of agro-climatic parameters. This study belongs to the spatial vulnerability
assessment category, and a lack of best practices in this field has been addressed using NDVI, SPI and LGP to
test and verify their utility for assessing agricultural vulnerability. A methodology was developed to determine
LGP from NDVI. The study’s results indicate that the northern and southern parts of Telangana registered asignificant increasing trend in LGP, while major centrally located districts cultivating high-value crops like
paddy, maize, sugarcane and cotton were vulnerable to reduction in LGP. Implementation of natural resource
management interventions in harvesting of rainwater and supplementing irrigation to minimize crop losses
would help reduce hardship and improve farmers’ adaptive capacity.
Key words: agro-ecological sub-region (AESR), length of crop-growing period (LGP),
Normalized Difference Vegetation Index (NDVI), rainfed agriculture,
Standard Precipitation Index (SPI), vulnerability

1. Introduction
Rainfed dryland agriculture is practiced in arid,semi-arid and hot, dry sub-humid tropical regions ofIndia that account for 76.74 million hectares (mha) of a
total geographic area of 90.4 mha in Peninsular India.
Telanganathe, the newest, 29th state in India (administrative unit – the basis for a federal government system
Global Environmental Research
18/2014: 161-170
printed in Japan

formed by bifurcation of erstwhile Andhra Pradesh state),
extends across 11.48 mha and accounts for 15% of thetotal area. It is a major agricultural region with apopulation of 35.3 million persons, of which 22.5 million
are rural inhabitants who depend directly on agriculture(Fig. 1).
The region receives an average annual rainfall of
900 mm (600–1,100 mm), which occurs in 50–60 rainy©2014 AIRIES162

K. RAMACHANDRAN et al.

India

LegendIndia Boundary
State Boundary
Telangana State

Fig. 1 Location of the study area.

days which are essentially associated with thunderstormsof short duration, causing runoff and soil erosion.
Drought and flood events are common and adversely
impact agricultural production. In recent years there has
been an increase in climate variability and extreme
weather events, like heat waves, thunderstorms associated with hail, unseasonal rain, etc., that repeatedly
affect agricultural operations in the region, making study
and analysis of climatic change essential. In order tounderstand trends in climatic variability, it was deemed
fit to study variations in the state of vegetative cover,
especially under agricultural land use, using satellite data,
which expresses the state of several bio-physical factors
influencing of vegetation in an integrated manner.
Permanent vegetation, such as forest cover, was masked,
as subtle changes would be difficult to assess given thenature of deciduous forest cover in study. Agriculturalland use in the form of anthropogenic interventions onland was analysed using Normalized Difference
Vegetation Index (NDVI) data products obtained fromthe AVHRR global dataset, as it indicates vegetation
vigour and dynamics and variations therein in a comprehensive manner. Tools and techniques of remote sensing
and GIS facilitate such spatial analysis when carried out
in India and elsewhere (Ramachandran et al., 2009;
NRSC, 2011; SeshaSai et al., 2011; de Sherbinin, 2014).
The current study is part of a research program initiated in 2011 to assess agricultural vulnerability in rainfed
regions of India. It was funded by the Indian Council ofAgriculture Research (Ministry of Agriculture, Government of India) under the National Initiative for Climate
Resilient Agriculture. Agricultural R & D in recent times
in India has been guided by climate change research,which has dominated political, economic, social andinternational discussions and dealings world-over since
the severe drought events of 1979 and 1987 that impacted
millions of humans and livestock in India and across theworld, and IPCC reports ascribe aberrant weather
situations across the world to anthropogenic causes suchas increasing GHG emissions (IPCC, 2007a, b, c, 2008,2012; US-EPA, 2014). Large-scale land-use andland-cover change (abbreviated as LULC below) has
impacted the state and vigour of vegetation and crops
adversely, leading to lower agricultural potential. Several
concepts, frameworks (Turner et al., 2003a, b) and indicators (US- EPA, 2014) have been put forward forclimate change studies, using various methods andassumptions for varied purposes (Schroter et al., 2004;
Preston et al., 2011; USAID, 2013; de Sherbinin, 2014).
By and large vulnerability of human society or a sector
thereof to climate change has been studied using
socio-economic approaches (Adger & Kelly, 1999) or
bio-physical approaches (Olsen et al., 2000) or integrated
approaches combining both (IPCC, 2007).
Vulnerability has been defined as the degree to whicha system is susceptible to or unable to cope with adverse
effects of climate change, including climate variability
and extremes. Thus, vulnerability is considered to be afunction of the character, magnitude and rate of climate
variation to which a system is exposed, its inherent
sensitivity, and its adaptive capacity, that enables recovery and /or the ability to withstand impact (McCarthy
et al., 2001; Fussel & Klein, 2006). There has been wide
agreement that analysis of adaptive capacity is supported
by socio-economic approaches and the study of sensitivity, by biophysical approaches, as both are internal dimensions of ecosystems while the aspect of exposure
deals with external dimensions or occurrence of bio-Use of NDVI to Assess Variability in Length-of-Crop-Growing-Period

physical phenomena like droughts, floods or cyclones.
An integrated approach, however, is what is desirable toaddress the issue of climate change, although there aresevere limitations in the present state-of-knowledge andunderstanding owing to inherent issues of scale, linkages,
telescoping connections and scenarios of future projections, and a lack of authentic datasets leading to a lack ofstandard methods for combining biophysical aspects withsocio-economic approaches to help in policy-making(Deressa et al., 2008). These lacunae have impacted thedevelopment of spatial vulnerability assessments or
mapping of vulnerability the world over, although it is a
useful tool for assessing the sensitivity of populations
and ecosystems to climatic stresses and the interplay ofspatially-related adaptive capacities (USAID, 2013; deSherbinin, 2014).
The present study acknowledges these issues andstrives to bring insights into the sensitivity of rainfed
agriculture in India to climatic variability through a studyof the NDVI vegetation index, to get an understanding ofclimate change phenomena and their adverse impact on
agriculture, dependant populations, livestock and theeconomy. The Standard Precipitation Index (SPI) wasused instead of actual precipitation data and LULC was
analysed to understand variations in LGP.
Integrating bio-physical data for analysis of vulnerability as a result of climate change requires a spatial
framework for vulnerability assessment (abbreviated as‘Spatial VA’ here). Although numerous Spatial VA studies have been reported, there is no consensus on bestpractices to be followed. US-EPA (2014) has used a set ofindicators suitable to the USA, while ATEAM-Germany(Schröter et al., 2004) have used indicators suitable forEurope. USAID in Africa (de Sherbinin, 2014) has used a
different set of indicators including NDVI and SPI as
exposure indicators and length of crop-growing period(LGP) as a sensitivity indicator, broadly following the
IPCC framework. US-EPA (2014) has grouped indicators
under six headings and used LGP as an indicator to assess
the impact on health and society, and leaf and bloom
dates, akin to vegetation vigour, as an ecosystem indicator.
In view of these various approaches, authors of thispaper decided to assess the sensitivity of rainfed agriculture using NDVI, and correlate it with variations in SPI
instead of actual rainfall, to gain an understanding ofresultant variations in LGP that manifest in the form of
increases/decreases in food grain, fodder and fibre production in India, broadly following the IPCC framework(Parry et al., 2007). As a result a methodology evolved
which loosely fits into the Extended Vulnerability
Framework of Turner et al. (2003a, b), Brinkmann(2006) and IPCC's Special Report on Climate Extremes(SREX) risk management for analysing the impact ofdrought/flood events and general variability in rainfall on
rainfed dryland agriculture in Telangana.
NDVI datasets with varied ground resolutions haveoverlapping scales and thus contribute to nested analysis,
but for lack of expertise in this field, the present study has

163

not established clear tele-connections between variations
in NDVI from agricultural land and land-use change due
to anthropogenic factors, although it falls in the domain
area of the Coupled Human-Environment System
Framework. Our study was restricted to examining
variations in NDVI and SPI as exposure indicators andchange in LGP as a sensitivity indicator to grasp theextent of agricultural vulnerability in rainfed regions inIndia without assessing possible variations under different climatic scenarios. As our study mainly focuses on
analysing variability in stressors, viz., drought and floods,
through SPI on the NDVI vegetation index by examining
past trends and assessing their impact on variations in
LGP impacting agricultural production, it may be insufficient for elucidating their impact under different
socio-economic scenarios (Preston et al., 2011; IPCC,
2012; USAID, 2013; de Sherbinin, 2014; US-EPA,
2014).

2. Materials and Methods
The objectives of this study were to identify trends inlength of crop-growing period (LGP) in agricultural land
use in the Telangana region derived from time-series
NDVI datasets and to identify areas that were agriculturally vulnerable based on variations in NDVI due to climate variability and frequent extreme weather events
indicative of climate change. Large-scale changes in
LULC and resulting variations in NDVI can be used asindicators of land-use intensification, desertification andclimate change or variability (Celis et al., 2007). Interventions like watershed development programs undernatural resource management have been shown to be
useful strategies for improving the adaptive capacity offarmers in the event of climate change (Ramachandran
et al., 2009; Wani et al., 2011).
A temporal study of NDVI variations was carried out
using NDVI products from NOAA-AVHRR (15-day,
8 km) which is part of GIMMS and is available from1982 till 2006 after corrections and can be downloaded
from the Global Land Cover Facility (GLCF) website<www.landcover.org> (http://www.glcf.umd.edu/data/
gimms/) as the 15-day Maximum-Value Composite.
Time-series NDVI datasets were used to assess thesensitivity of cropping systems in various agro-ecosub-regions (AESR) that are typical climatic zones
delineated using soil quality, soil water-holding capacity,moisture index and LGP, estimated using the FAO model(FAO, 1983; Velayutham et al., 1999). SPI estimatedfrom actual rainfall data was used to corroborate thesensitivity of agriculture during the study period (Tucker
et al., 1985; Mckee et al., 1993; Thenkabail et al., 2004;
Dadhwal, 2011; Saikia & Kumar, 2011).
NDVI from AVHRR with red reflectance in Band 1
and NIR reflectance in Band 2 was calculated as (Band 2– Band 1) / (Band 2 + Band 1) as it takes advantage oftypical low reflectance values of vegetation in the red
wavelength range, which corresponds to chlorophyll
absorption, and high reflectance values in the NIR range,

164

K. RAMACHANDRAN et al.

which signifies leaf structure, thereby enhancing thecontrast between vegetated, un-vegetated and sparsely
vegetated areas that are typical of the Telangana region.
LULC analysis helped in identifying NDVI contrasts
among agriculture, forest and open-scrubland. Bimonthly NDVI images were stacked and pre-processed,
followed by identification of the pixel-wise maximum
NDVI for estimating the maximum greenness of any
pixel during the corresponding year (1982–2006). The
8 km ground resolution of AVHRR permitted identification of agricultural vulnerability at a regional scale as it
was available for a relatively longer period of 26 years(1982–2006) and in line with most climatic analysis
requiring a 30-year time-series dataset; hence MODISTERRA NDVI data products with 250 m ground resolution available from 2001 were not selected. The coarse
resolution of the AVHRR data, however, increased thelikelihood of mixed pixels being classified erroneously,
thus adversely affecting accuracy in estimating areas
under agricultural vulnerability. A methodology wasdeveloped for estimating LGP using NDVI based on
White et al. (1997) and reported in Ramachandran et al.
(2014).
The India Meteorological Department (IMD) provides daily rainfall data spanning more than 100 years formany stations, and the present study used daily gridded
rainfall data (1901–2007) developed by Rajeevan et al.
(2008) for 661 rain-gauge stations in the study area.
Gridded rainfall data at 1° latitude x 1° longitude wereused to calculate SPI and long-term precipitation records
were fitted to a probability distribution and transformed
into a normal distribution, so that the mean SPI for any
location and desired period, were equal to zero. Positive
SPI values indicated greater than median precipitation,
while negative values indicated less than median precipitation. As SPI was normalized, both wetter and drier
climates could be presented in a similar manner and wet
and dry periods denoting flood and drought could beassessed (McKee et al., 1993). An SPI of ≤ 1.00 for any
given period was considered the start of a reduced rainfall
period that could lead to drought if prolonged; drought

was indicated when the SPI was continuously negativeand reached –1.0 or less, and it ended when the SPI
turned positive. The SPI was used to identify drought andflood events and the corresponding NDVI reflectancecoefficient to study variations in LGP.
Spatial rainfall patterns were obtained by a kriging
interpolation of rainfall data to obtain an 8 km resolution
corresponding with the ground resolution of the AVHRRNDVI dataset. Analysis indicated that rainfall was
highest during July–August while max. NDVI occurred
subsequently in the months of September–October, and1982 was considered the base year for the study.
The dates of start-of-season (SOS) and end-of-season(EOS) of LGP were identified using an NDVI reflectancecoefficient of 0.5 (White et al., 1997; Vrieling et al.,2008), which was higher than that usually obtained fromvegetation cover in semi-arid and dry sub-humid regionsof India. Hence typical NDVI reflectance coefficientvalues were generated for each AESR (agro-ecologicalsub-region) of Telangana. The NDVI threshold value was
identified for each AESR using the average NDVI valueof three normal years (when annual/ seasonal rainfall
equalled the long-term average) for each crop growing
season, viz., the Kharif (summer monsoon) and Rabi
(post-monsoon) of 1986, 1991 and 1999. To find thethreshold value, the actual NDVI value of the 15-daycomposite for each AESR was plotted. The SOS for the
Kharif season was estimated by extracting and stacking
the 15-day NDVI composite from June–October for eachyear and the SOS threshold was assumed as the value
after which NDVI showed an increasing trend. The EOS
for the Kharif was identified as the fortnight when the
NDVI value fell, continuing a decreasing trend. The
point when the NDVI coefficient fell continuously was
taken as the threshold value of the EOS. For analysing
LGP variations during the Rabi season, NDVI composites of November to March were taken and the SOS andEOS were identified similarly (Ramachandran et al.,2014). The results of this study can help in formulating
suitable packages of practice for farming in the region(Fig. 2).

Pre-processing

Methodology for analyzing LGP using NDVI
Year-wise stacked NDVIImages (1982 – 2006)
Subset NDVI stack with AESR

Individual AESR shape-file

AESR-wise LGP Threshold Value derived
from Mean of 3 normal years(1986,1991,1999)

AnalysisIf pixel value of each Kharif/Rabi NDVI image >TV then ( pixel value =1
otherwise 0)
Kharif /RabiImages stacked and segregated with pixel values (0/1)

Stack Sum of Kharif /Rabi 15-day composite image

ResultMultiply Kharif /Rabi LGP with 15 to convert pixel value to days

Kharif/Rabi LGP in no. of days

Fig. 2 Methodology for analysing LGP using NDVI (after Ramachandran et al., 2014).

Use of NDVI to Assess Variability in Length-of-Crop-Growing-Period

3. Results and Discussion
3.1 Assessment of agricultural vulnerability using
NDVI from the AVHRR dataset
In semi-arid tropics, water is the major constraint tocrop production (Celis et al., 2007; CRIDA, 2014). In
India the southwest monsoon is the predominant sourceof water for agriculture during the Kharif season. The
mean and standard deviation of max. NDVI were estimated for agricultural land use to gain an understandingof variability in greenness that indicates crop vigour andhealth and correlates highly with crop yield in the season,
as mentioned earlier. The pixel-wise max. NDVI indicates maximum greenness during the corresponding year
(1982 to 2006) and variability in greenness was taken as
indicator of vulnerability. The coefficient of variation ofmax. NDVI was calculated and used to indicate agricultural vulnerability (Ramachandran et al., 2013) (Fig. 3).
The general trend in rainfall and NDVI in India denoting
variations in vegetation dynamics in the country owing toclimatic variability is indicated in Fig. 4. It formed thebasis for identifying the spatial extent of agriculturalvulnerability in India (Ramachandran et al., 2013).
3.2 Pattern of LGP trends in Telangana
The Telangana region comprises three major agroecological sub-regions (AESR), which have been given
unique nomenclature (Velayatham et al., 1999) based on
their agro-climatic parameters, as stated earlier.

165

The three AESR in Telangana have the following
designations and characteristics: AESR 6.2, denoting
central and western Maharashtra Plateau, northern
Karnataka Plateau and northwestern Telangana Plateau
with a hot moist semi-arid climate and shallow to
medium loamy and clayey black soils having waterholding capacities of 100–200 mm with a normal LGP of
120–150 days estimated using the FAO model (1983);
AESR 6.3, denoting a region comprising eastern
Maharashtra Plateau with a hot semi-arid climate,medium to deep clayey black soils having water-holding
capacities of 100–200 mm and a normal LGP of 120–150
days; and AESR 7.2, encompassing a major part of theTelangana Plateau with a hot, moist semi-arid climate
and deep loamy and clayey mixed red and black soils
with water-holding capacities ranging from 100 to >200
mm and a normal LGP of 120 –150 days. Parts ofTelangana falling under AESR 6.2 are the districts ofMedak, Karimnagar and Nizamabad, while half of the
Adilabad district falls under AESR 6.3 while the rest ofTelangana falls under AESR 7.2, comprising the districtsof Mahabubnagar, Nalgonda, Warangal, Khammam,
Rangareddy and Hyderabad. As mentioned earlier, NDVI
(reflectance coefficient) values of normal years weretaken to identify the threshold value for SOS and EOS for
the Kharif season in each AESR, which were estimated at
0.25 for AESR 6.2, 0.26 for AESR 6.3 and 0.33 for
AESR 7.2 (Ramachandran et al., 2014) (Fig. 2).
The NDVI dataset of the Kharif season (10 in a year)(NOAA - AVHRR dataset 1982 – 2006)

Fig. 3 Extent of agricultural vulnerability in India (after Ramachandran et al., 2013).

166

K. RAMACHANDRAN et al.

pertaining to June-September was stacked and the pixelwise sum of the NDVI was derived. This value wasmultiplied by 15 (number of days of the NDVI composite) to derive LGP in number of days.
This method was applied to Kharif season images for
1982–2006 to study deviations in LGP, as a result ofvariations in SOS and EOS in the three AESRs in
Telangana, and variations in LGP were compared withthe value of normal years. LGP values were classified

according to types of crops cultivated in a given period
and variability in the extent of area under each class during the study period. LGP values in various AESRs in
Telangana ranged from <60 days in the south of
Mahabubnagar with an average SPI of 0.02 and districtsof Nalgonda (SPI of 0.22) to over 150 days in northern
districts of Khammam (SPI of 0.47) and Nizamabad (SPIof 0.15) (Fig. 5a, b). Figure 6 indicates that the area under
the LGP class of 120 –150 days was largest in 2001 (SPI

Fig. 4 Variations in all-India rainfall and NDVI (after Ramachandran et al., 2013).

(a)
150

AESR 6.3& 6.2

No. of Days

140
130
120
110
100
90

Adilabad

Karimanagar

Medak

Nizamabad
2003

2004
2004

2006

2002

2003

2005

2001

2002

1999

2000

1998

1999

2001

1997

1998

2000

1996

1997

1995

1996

1994

1995

1993

1994

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

80

(b)
No. of Days170

AESR 7.2

150
130
110
90
70

Hyderabad

Khammam

Mahabubnagar

Nalgonda

Rangareddy

2006

2005

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

50

Warangal

Fig 5 (a) Variations in LGP in the northern Telangana region (AESR 6.3 & 6.2).
(b) Variations in LGP in the southwestern Telangana region (AESR 7.2).

600

400
300
200
100

Fig. 6 Trends in LGP classes in Telangana (1982–2006).

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

0

1982

Area (0000'ha)

LGP Trend - Telangana

<60
60-90
90-120120-150500

Use of NDVI to Assess Variability in Length-of-Crop-Growing-Periodof –0.22) and 2006 (SPI of 0.748) indicating near normal
rainfall for crops like paddy, maize, pigeon pea, soybean,cotton and sugarcane. The SPI ranged from –1.46(moderately dry) in 1985 to 0.75 indicating near normality of 2006. In 2001 the agricultural area under this classof LGP was 5.5 mha, indicating good agricultural production during that year. The area under the LGP class of
90–120 days was over 4.0 mha in 1998 and 2003 when an
SPI of 0.44 indicated near normal rainfall.
The area under the LGP class of 60–90 days increased
in 1994 to 4.4 mha when the SPI was – 0.97, indicating near normal for the Mahabubnagar, Nalgonda,
Rangareddy and Warangal districts. In 1984 and 1997 the
LGP class of <60 days extended across 1.9 mha and the
SPI was –0.21 denoting near normality in the dry semiarid tract of Nalgonda, Rangareddy and Mahabubnagar.
Figure 7 shows temporal variations in LGP in various
districts of the Telangana region. As Mahabubnagar andNalgonda were identified as marginally vulnerable with acoefficient of variation of max. NDVI < 10 –20 and the
LGP showed an increasing linear trend, it was essential toanalyse this variability using a Mann-Kendall Test performed at two levels ‒ AESR and district. It was seen that
under AESR 6.2 and 6.3, covering northern Telangana,the LGP derived from the NDVI demonstrated a significantly increasing trend (significance of p= 0.0002** &p= 0.0038** at the 1% level) while under AESR 7.2,
denoting southern Telangana, the LGP indicated a lower
significance (p = 0.04* at the 5% level) (Table 1). Our
study indicated that out of the ten districts of Telangana,
4.82 mha in Adilabad, Mahabubnagar and Nalgonda
showed an increasing trend in LGP while other districts
showed no significant trend (Table 2). In the case of the
Karimnagar and Warangal districts, covering 2.47 mha ofprime agricultural area, where cash crops like groundnut,
cotton and maize are grown, decreasing LGP could
adversely affect the regional economy.

167

4. Conclusions
Time-series NDVI datasets from NOAA-AVHRR(15-day, 8 km) which helped in identifying agriculturally
vulnerable regions in India, were used to assess variations in length of crop-growing period (LGP) that induced agricultural vulnerability in rainfed areas of theTelangana region. The SPI was used to corroborate trends
in the NDVI. An increase in spatial extent under the LGP
class of 120–150 days, which is favourable for major
cereals, pulses and cash crops, viz., paddy, maize, pigeon
pea, soybean, cotton and sugarcane, was suitable for
agriculture. However an increase in extent under the LGP
class of 90–120 days that facilitates cultivation of maize,
pearl millet and mustard could adversely affect crop
production. Crops under the LGP class of 60 –90 days aremajor oilseeds, viz., groundnut, sunflower and castor,and millets like sorghum and pearl millet. A decrease in
rainfall and shorterning of LGP, however, would affectthese crops. An LGP of <60 days denotes failure of themonsoon and losses to agriculture, as only pearl millet
and cluster beans can be cultivated. Regionally, southern
Telangana is drier with a shorter LGP and hence more
vulnerable to climate change. Districts of northern
Telangana with favourable SPIs have longer LGPs andare hence suitable for cultivation of cereals like paddy
and maize and cash crops like cotton and sugarcane. A
reduction in LGP, however, would cause hardship tofarmers and adversely affect the economy. A closer lookat variations in LGP using the Mann-Kendall Test
indicated that LGP was increasing in Adilabad, Nalgonda
and Mahabubnagar while it was decreasing in the
Karimnagar and Warangal districts, which are prime
agricultural districts cultivating high-value cash crops.
Our study indicated that variability in LGP could
adversely impact rainfed agriculture and in order tominimize the impact of climate change, NRM
interventions like watershed development could be useful for improving adaptive capacity among farmers.

Table 1 Mann-Kendall analysis of LGP variations at the AESR level.

AESR
6.2
6.3
7.1
7.2

S
158
171
130
87

Z
3.667
2.892
3.013
2.009

P
0.00020.00380.00260.0445EquationY = –1507.7 + 0.7895X
Y = –1044.8 + 0.5518X
Y = –854.43 + 0.4486X
Y = –646.68 + 0.3498X

Significance
**
**
**
*
**: p<0.01, *: p<0.05

Table 2 Significance of variability in LGP at the district level, Telangana.

AESR
6.3
7.2
6.2
7.2
7.2
6.2
7.2
6.2
7.2
7.2

District
Adilabad
Hyderabad
Karimnagar
Khammam
Mahabubnagar
Medak
Nalgonda
Nizambad
Rangareddy
Warangal

S
95
97
–34
61
103
69
93
38
77
–11

Z
2.196
1.718
–0.771
1.402
2.383
1.589
2.149
0.865
1.775
–0.188P
0.02810.08570.44060.161
0.01720.11220.03160.387
0.07580.8511EquationY = –794.10+0.4588X
Y = –1157.8+0.6250X
Y = 551.05–0.2122X
Y = –885.50+0.5089X
Y = –2001.9+1.046X
Y = –790.60+0.4582X
Y = –1634.6+0.8675X
Y = –91.361+ 0.1127X
Y = –1481.7+0.7941X
Y = 166.56–0.2735X

Significance
*
NS
NS
NS
*
NS
*
NS
NS
NS
*: p<0.05

168

K. RAMACHANDRAN et al.

Fig. 7 Temporal variations in LGP at the AESR level in Telangana (1982–2006).

Use of NDVI to Assess Variability in Length-of-Crop-Growing-Period

Acknowledgements
The authors acknowledge research grants provided by
ICAR under the National Fellow scheme to the firstauthor and under the NICRA project to CRIDA. They
also thank the Director of CRIDA for providing facilities
to carry out this research study. The authors acknowledge
the contributions of Mrs. M. Gayatri and Mrs. N.
Thilagavathi (former project staff) for their contributions
and Mrs. A. Sravani for secretarial assistance.
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Kaushalya RAMACHANDRANKaushalya RAMACHANDRAN is a principal scientist & ICAR National Fellow (in geography). She
was a DAAD Fellow and has a doctorate from the
University of Saarland, Germany. She is involved
in developing strategies to improve adaptability offarmers in rain-fed regions. The Global Land
Project –IGBP-IHDP– endorsed her research
programme in 2008. She is a member of the Indo-German collaboration
for sustainability research and has contributed to GEO-5, IHDP and the
UNU-Bonn Conference on Environment, Forced Migration and Social
Vulnerability. She was conferred the ICAR best woman scientist award in2006.

Shubhasmita SAHANI
Shubhasmita SAHANI is a research associate at
CRIDA under the ICAR National Fellow scheme.
She is currently pursuing her doctorate at theCenter for Spatial Information Technology, JNTUHyderabad, India. Her area of interest is the studyof coupled human – nature interactions throughremote sensing and development of applications
using open source tools & libraries.

Praveen Kumar VADDI
Praveen Kumar VADDI is a senior research fellow
at CRIDA in the ICAR funded NICRA program.
He is working on assessment of the impact ofclimate change on agriculture using GIS andremote sensing.

Kalaiselvi BHIMAN
Kalaiselvi BHIMAN is a soil scientist at the
National Bureau of Soil Survey and Land Use
Planning, Nagpur, Maharashtra, India. She hascompleted her doctorate from Tamil Nadu Agricultural University where she specialized in thefield of soil fertility. She is working on GIS
applications for land-use planning.

Satish JANGAM
Satish JANGAM was formerly a senior research
fellow under the ICAR National Fellow scheme at
CRIDA. He worked on application of remotesensing and GIS for assessing the potential ofrainfed agriculture.

(Received 1 July 2014, Accepted 30 November 2014)