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Geospatial analysis of the impacts of climate variables on crop productivity in Warangal district of Andhra Pradesh, India.


Climate change and agriculture are interrelated processes, both of which take place on a global scale. The effect of climate on agriculture is related to variability in local climate rather than in global climate patterns. Due to the variability of climate in the recent past, more intense and longer droughts have been observed over wider areas since 1970, particularly in the tropics and subtropics (IPCC, 2007) [1]. Agriculture is closely dependent on the endowment of natural resources and environmental conditions of soil and climate. India is divided into many agro--climatic zones depending upon rainfall, agriculture and soil fertility. The agro climatic conditions of India and the various other factors that largely determine the cropping patterns vary throughout the country. Andhra Pradesh has seven agro-climatic zones based on the climatic parameters viz. rainfall, temperature, and soil type. Warangal district falls into two agro climatic regions namely Southern Telangana Zone and North Telangana Zone. The present study attempts to identify the impacts of variation in climate on agricultural productivity and agricultural GDP in the last few years in Warangal district of Andhra Pradesh using statistical techniques.

Study Area

The study was carried out in Warangal district of Andhra Pradesh, and is located 148km northeast of the state capital of Hyderabad. Warangal district with a total geographical area of 12,846 sq km lies between the latitude 17-19' and 18-36' North and Longitudes of 78-49' and 80-43' East and is above the mid sea level by 870ft, bounded on the North by Karimnagar district, West by Medak district, South by Nalgonda district and by Khammam district on East and South-East. It has a population of nearly 3,522,644 Lakhs. The district is mainly agrarian and agriculture is the main stay of the population. The district has 51 mandals and the distribution of the agro climatic zones in the district is shown in Fig 1.


Materials and Methods

The analysis requires meteorological data viz: rainfall and temperature along with agriculture related data viz: yield, farm harvest prices, production index number etc., which are obtained from departments of Indian Meteorological Department and District Economics and Statistical Department respectively for a study period 1980-2008. SYSTAT 7.0.1 was used for carrying out the statistical analysis of the data collected. [R.sup.2] and linear regression analysis along with cluster analysis has been carried out. The study used data at district and mandal/block level. At the district level the yearly mean has been computed to identify the period of low or high rainfall epoch. The statistical parameters mean, standard deviation, percentage deviation, coefficient of variation and regression for rainfall data have been computed for every year and also for seasonal variation for monsoon (June-October), non monsoon (November-May) and annual periods at mandal levels. The spatial representation of the data was done using Arc GIS 9.1 software. Cluster analysis for estimating the impact of climate variability on yield was carried. Ordinary Least Square Model was used to verify the results obtained in cluster analysis and its impact on agricultural GDP.

Results and Discussion

The analysis of the climatological data and its impact on agricultural yield for a period of 25 years is discussed in this paper. The climatological data refers to the temperature and rainfall data collected at both the district and mandal levels of the district. The maximum and minimum temperature data for a period of 1999 to 2008 was analyzed and from the data it is found that there is an overall increase of the temperatures in the district of Warangal. The increase in the mean maximum temperature is by 0.6[degrees]C while the minimum temperature increased is by 0.7[degrees]C in the same period. Similar trends have been observed in a study presented by Sinha S.K and Swaminathan M.S (1991) [2] in which trends of change in the actual temperature in North India have also shown an increase. Sinha S.K et al (1998) [3] have brought out that while the mean air temperatures over the wheat growing regions were higher by 1.7[degrees]C over a period of 15 days (January 16-February 1), the actual temperature rise was 2.3 to 4.5[degrees]C in the major wheat producing region of Punjab and Haryana. Earlier work of Hingane L.S et al (1985) [4] on long-term trends of surface temperature covering the period of 1900-82 from 73 well distributed stations also showed a warming trend of 0.04oC per decade for the period 1901-82. Saseendran S.A et al (2000) [5] showed that for every one degree rise in temperature the decline in rice yield would be about 6% in Kerala, India. The study carried out by Deka R.L et al (2009) [6] examines long-term changes and short-term fluctuations in ambient temperature of North East India during 1901-2003 located in part of the Eastern Himalayan region of the Brahmaputra Basin. The results showed significant increase in annual and seasonal maximum temperature in North East India.

After the analysis of temperature data was carried, the district level annual distribution of rainfall for the period 1980 to 2008 was attempted. Figure 2 shows the annual average rainfall distribution for the district during the study period. The rainfall pattern has been erratic. It can be seen that large deviation in rainfall has occurred over the last few years. The annual rainfall variability in the region stretches from 624.5 to 1482.9 mm.


A similar study on the annual rainfall distribution pattern of Umiam, Meghalaya, India was carried out for the period of 27 years (1983-2009), which indicates similar trend and significant erratic nature in the annual rainfall Anup Das (2009) [7]. The trend analysis of rainfall data from 1140 meteorological stations carried out at CRIDA have shown a negative trend among the stations situated in deep southern parts, southern peninsular, Central India, Parts of North Indian region and North East regions. Positive deviations are seen at Gujarat, Maharashtra, Coastal Andhra Pradesh, Rayalaseema and Orissa Rao G.G.S.N (2007) [8].

In the present study it is observed that there is significant deviation in the rainfall recorded in the year 1983-1984 & 1989-1990 where it exceeds the normal rainfall of 993 mm showing 40.39% & 49.34% respectively (Fig 3). In the year 2002-2003 the actual rainfall recorded is 624.5mm showing -37.11% deviation from normal. The overall trend shows a decrease in the rainfall for the region. From the above findings it is clear that there is a change in the rainfall patterns. At the mandal level the mean annual rainfall along with the coefficient of variation values were calculated for 51 mandals. It was observed that mean data of rainfall for the mandals falls in the range of 545.9 to 1315.8 mm. Similar to the district level data analysis the trends are negative for 28 mandals compared to the normal rainfall and the remaining showed the positive trends (Fig 4). The rainfall in Warangal district is predominantly attributable to south west monsoon. Based on the mandal wise analysis of the coefficient of variation six mandals have been chosen for further in depth analysis of data. Out of the six mandals, Bachannapeta and Maddur fall in the Southern Telangana Zone while the remaining Eturnagaram, Bhupalpalle, Tadvai and Hanamkonda fall in the North Telangana Zone. Table 1 shows the coefficient of variation, percentage deviation, Y values & range of [R.sup.2] values for the 6 mandals, the values for Bachannapeta which recorded a high of 0.258 was exceptional while all other values are ranging from 0.001 to 0.061. Guhathakurta P et al (2008) [9] have prepared a time series of contribution of rainfall for each month towards the annual total rainfall for each year for all over India.



The analysis presented thus far shows considerable deviations in the rainfall patterns and hence an analysis of the impact of crop yield was taken up. Agriculture is adversely affected not only by an increase or decrease in the overall amounts of rainfall, but also by shifts in the timing of the rainfall. In tropical agricultural areas, similar assessments indicate that yields of some crops would decrease with even minimal increases in temperature because they are near their maximum temperature tolerance (IPCC, 2001) [10]. The crops paddy, cotton and groundnut were selected and analyzed for the impact of rainfall on agricultural productivity for a period of 15 years. In order to understand the relation between rainfall and crop yield a correlation between the year wise annual rainfall and the crop yield data was carried out for three crops. The present study attempts to correlate the annual rainfall and agricultural yield for the crops grown in the district (Fig. 5). It was observed that the productivity for crops like groundnut and cotton have a mixed response to rainfall and needs further correlation with other parameters like micro nutrients, fertilizers etc. however paddy has a direct relation with rainfall patterns and yield. A more general approach to obtain a clustering of the time series data of rainfall on crop yields was attempted. The analysis identifies clusters of years with similar level of crop yields. The association of these crop yield clusters with those of rainfall patterns was identified through several clusters. Variables that are clustered in different clusters are represented in table 2. Dendrogram were generated to understand the clustering pattern. Michelle et al (2004) [11] observed that climate change is likely to affect agriculture in Norway. The effect on yield per decade varied with geography and crop. There was a negative yield response to increased precipitation in many parts of the Norway.


It was observed from the cluster analysis in the present study that distinct new clusters are created with most impact on the rainfall data. The impact for paddy was higher and more related to rainfall patterns than that observed for groundnut and cotton. Thus it can be concluded that the direct impact of rainfall on paddy crop is more while that is not true for crops of groundnut and cotton.

Despite technological advances such as improved crop varieties and irrigation systems, climate still plays a key role in Indian agricultural productivity thereby national prosperity Rao G.G.S.N et al (2008) [12]. A multivariate approach that identifies the pattern of rainfall during the entire crop growth periods of the crop season was proposed by Kulkarni.B.S (2004) [13]. To understand the impact of agricultural yield on agricultural GDP the Ordinary Least Square Model (OLS) model was used for the three crops in the district of Warangal in the same study period as for cluster analysis. The impact of climatic variables on the agricultural GDP was studied by using prices, rainfall and production data of three crops which showed a significant positive relationship as shown in Table 3. The study shows that there is high impact of rainfall over production and prices more for paddy than the other two crops selected in this study. Preeti Laddha et al (2007) [14], have carried out a similar study using OLS Model & Co--Integration Model to measure and analyze the impact of weather on commodity prices and consequent linkages to inflation, exchange rates and GDP for five major crops was discussed. Similar results have been obtained from the current study and further, the results obtained from the cluster analysis are collaborated with that of the OLS model studies for the three crops. The studies have shown that equilibrium does exist between GDP, price, rainfall and production in all the three cases.


Warangal district is divided into two agro--climatic zones, Southern Telangana Zone and North Telangana Zone. Out of total 51 mandals, 18 fall in Southern Telangana Zone while the remaining 33 mandals fall in North Telangana Zone. Temperature data was analyzed and an increase of both minimum and maximum temperatures has been observed. District level rainfall data for the period 1980 to 2008 was collected and analyzed and deviation in rainfall has occurred over the last few years with an overall decreasing trend. The pattern has been erratic. The mean data of rainfall for the mandals is in the range of 545.9 mm to 1315.8 mm. This trend analysis also carried out for the Warangal district by using mean annual rainfall and normal rainfall for the 51 mandals. From the range of [R.sup.2] values for the 6 mandals the values for Bachannapeta is the highest recorded as 0.258 and seems to have the highest impact, while all other values are in the range 0.001 to 0.061. Cluster analysis has shown direct impact of rainfall on paddy when compared to that of groundnut and cotton. The OLS models have also correlated with the results obtained for impact on productivity due to climatic changes and a positive correlation is observed between the two.


The authors wish to place on record their thanks for the support given by Ministry of Environment and Forests (MoEF), GoI, New Delhi for the conduct of the present study as a part of the research project awarded by MoEF.


[1] IPCC., Climate Change: 2007 "The Physical Science Basis," (Intergovernmental Panel on Climate Change Working Group I), IPCC Working Group I.

[2] Sinha, S.K., and Swaminathan, M.S., 1991, "Deforestation, Climate Change and Sustainable Nutrition Security: A Case Study of India," Climate Change, 19: 201-209.

[3] Sinha, S.K., Singh, G.B., and Rai, M., 1998, "Decline in Crop Productivity in Haryana and Punjab: Myth or Reality?," Indian Council of Agricultural Research, New Delhi, India, 89 p.

[4] Hingane, L. S., Rupa Kumar, K., and Ramana Murthy, Bh.V., 1985, "Long term trends of surface air temperature in India," Journal of Climatology, 5: 521-528.

[5] Saseendran, S. A., Singh, K. K., Rathore, L. S., Singh, S. V., and Sinha. S, K., 2000, "Effects of Climate Change on Rice Production in the Tropical Humid Climate of Kerala, India", Climatic Change 44: 495-14.

[6] Deka, R.L a+., Mahanta, Ca and Nath, K.K (b)., 2009 "Trends and Fluctuations of Temperature Regime of North East India," (a) Centre for the Environment, Indian Institute of Technology Guwahati, Assam, (b) Department of Agrometeorology, Assam Agricultural University, Jorhat,, ISPRS Archives XXXVIII-8/W3 Workshop Proceedings: Impact of Climate Change on Agriculture

[7] Anup Das., Ghosh, P.K., Choudhury, B.U., Patel, D.P., Munda, G.C., Ngachan.S.V and Pulakabha Chowdhury., 2009 "Climate Change in North East India: Recent Facts and Events--Worry for Agricultural Management," ICAR Research Complex for NEH Region, Umiam--793 103, Meghalaya, India, ISPRS Archives XXXVIII-8/W3 Workshop Proceedings: Impact of Climate Change on Agriculture.

[8] Rao, G.G.S.N., 2007, "Impact, Adaptation and Vulnerability of Indian Agriculture to Climate Change," Consolidated Report (2004-07).

[9] Guhathakurta, P., and Rajeevan, M., 2008, "Trends in the Rainfall Pattern over India," Int. J. Clamoto., 28:1453-1469

[10] IPCC 2001, "Climate Change 2001: Impacts, Adaptation, and Vulnerability," [Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change; summary for policymakers] Cambridge, Cambridge University Press, 1032 pp.

[11] Asbjorn Torvanger *., Michelle Twena and Bard Romstad., 2004 "Climate Change Impacts on Agricultural Productivity in Norway," CICERO, P.O Box 1129, Blindern, N-0318 OSLO, Norway.

[12] Rao, G.G.S.N., Rao, A.V.M.S., Vanaja, M., Rao, V.U.M. and Ramakrishna, Y.S. 2008. "Impact of Regional Climate Change over India," In: Climate Change and Agriculture over India (eds.) G.S.L.H.V. Prasada Rao, et al. AICRP on Agro-Meteorology, Hyderabad.14-50.

[13] Kulkarni, B.S., Sreenivasa Rao, T and Krishna Kanth, G., 2004, "A Study on Association of Combined Effect of Rainfall Patterns on Crop Yields," Acharya N.G. Ranga Agricultural University, J. Ind. Soc. Agril. Statist. 58(3), 2004: 344-351.

[14] Laddha, P., Agarwal, S., Kulkarni, P., and Murthy, N.K.A., "Weather Risk, Agro Commodity Prices and Macro Economic Linkages: Evidence from Indian Scenario using Co-integration Model," (IFIM, Bangalore), International Conference on Agribusiness and Food Industry in Developing Countries: Opportunities and Challenges, August 10-12 2007, Indian Institute of Management, Lucknow.

Dr. Valli Manickam *, R. Radhika and Dr. Iyyanki V Murali Krishna

Environment Area, Administrative Staff College Of India Bella Vista, Khairatabad, Hyderabad-500082, (A.P.) India E-mail:,,

* Corresponding Author E-mail:
Table 1: Coefficient of Variation in Six Mandals of Warangal
District of Andhra Pradesh

Mandals Years Percentage Coefficient of
 deviation Variation

Bachannapeta 1990-2008 -18.1 31.5
Maddur 1988-2008 3.9 26.9
Eturnagaram 1986-2008 2.7 20.8
Bhupalpalle 1990-2008 -11.7 25.4
Tadvai 1990-2008 4.0 20.3
Hanamkonda 1980-2008 -3.7 28.9

Mandals Y values [R.sup.2

Bachannapeta y = 2.4545x - 41.458 0.258
Maddur y = 0.9602x - 6.153 0.041
Eturnagaram y = 0.1051x + 1.534 0.001
Bhupalpalle y = -0.927x - 2.9002 0.049
Tadvai y = 0.9725x - 5.287 0.061
Hanamkonda y = -0.5756x + 4.6709 0.022

Table 2: Variables used in the cluster analysis of the rainfall
and crops

Variables Annual June-October Paddy Yield
 rainfall rainfall (SWM) (kg/ha)


Variables Groundnut Cotton
 Yield Yield
 (kg/ha) (Kg/ha)


Table 3: Observations of Ordinary Least Square Model

Commodities Farm Harvest Prices, Production
 Index number, Annual rainfall

 Multiple R R Square t stat

Paddy 0.26 0.07 1.96
Groun dnut 0.56 0.31 2.02
Cotton 0.14 0.02 4.65

Commodities Farm Harvest Prices, Production
 Index number, Annual rainfall, GDP

 Multiple R R Square t stat

Paddy 0.72 0.52 2.07
Groun dnut 0.81 0.66 2.43
Cotton 0.57 0.33 0.50
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Author:Manickam, Valli; Radhika, R.; Krishna, Iyyanki V. Murali
Publication:International Journal of Applied Environmental Sciences
Geographic Code:9INDI
Date:Sep 1, 2012
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