INDIA AGRICULTURE AND CLIMATE DATA SET

Prepared by

Apurva Sanghi, K.S. Kavi Kumar, and James W. McKinsey, Jr.

(This data set was compiled and used in the study "Measuring the Impact of Climate Change on Indian Agriculture". The study was funded jointly by the World Bank Research Budget and by the Electric Power Research Institute in Palo Alto, California. The research findings were published as a World Bank Technical Paper No. 402)

 

 

INTRODUCTION

 

Most present- and future- environmental problems such as climate change and conservation of biodiversity are global in scope and require rigorous empirical analyses for any meaningful policy formulation. Though it is relatively easy to access data related to developed countries, data from developing countries is rather difficult to acquire. Both availability and accessibility of such information hinders potential researchers from working on developing country case studies. A classic example in this regard is India. India has one of the world’s largest network of government, semi-government and private organizations for collecting and reporting various agricultural, climatological, hydrological and economic statistics. However, often it becomes difficult for researchers/policy-analysts to access and use this huge amount of data for the following reasons:

 

 

With the above issues in mind, this project has brought together various data pertaining to Indian agricultural, climatological, edaphic and geographical variables over a period of over 30 years, starting from 1957-58. The focus is to put this comprehensive information in one single place and in an easily accessible form, namely, the world-wide web. The remainder of this section provides a brief discussion about the scope, sources and potential uses of the database.

 

 

 

Scope of the database

The database presents district level data, as districts are the smallest administrative units in India at which consistent and reliable data is available. As many as 271 districts, covering 13 major states in India, constitute the database. Thus, area-wise, the database covers more than 85% of India and with the exception of the Southern state of Kerela and the North-eastern state of Assam, the areas not covered are relatively some of the least important from the agricultural perspective. Most of the data has been presented for about 30 years, from 1957-58 to 1986-87.

Agricultural data covered in the database include, among other variables, area planted, production and farm harvest prices for five major and fifteen minor crops. Areas under irrigated and high-yielding varieties (HYV) is provided for the major crops. Data on agricultural inputs, such as, fertilizers, bullocks and tractors - in both quantity and price terms - is also provided. Other variables covered are agricultural labor, cultivators, wages and factory earnings, rural population and literacy proportion.

Meteorological station level climate data (average climate over 30 year period) has been used to generate district specific climate using sophisticated surface interpolation techniques. Similarly, soil data, compiled through various tables and maps, is also included in the database. Given the absence of short-term variability in climate and soil variables, data pertaining to these variables do not exhibit any inter-temporal variability; consequently they are purely cross-sectional in nature.

 

Sources of data

A number of national and international sources have been used in compiling the database. For agricultural data, the main source was the data set created by James W. McKinsey, Jr. and Robert Evenson (Yale university, CT, USA). As discussed later in the report, a number of corrections have been made in the original data set by cross-checking the same with government publications. Some of the main government publications used in compiling the database are:

 

Climate data from over 160 meteorological stations are from the Food and Agricultural Organization (FAO) of the United Nations. Data on edaphic variables from various soil maps and publications was meticulously compiled by McKinsey.

 

 

Potential uses of the database

Agriculture comprised approximately 30% of Indian GDP in 1994. As Indian agriculture is a major issue of interest for researchers and policy analysts world over, the present database with its coverage and easier accessibility is expected to fill a much awaited gap.

For example, it is widely accepted that for estimating potential impacts of climate change, the major bottleneck is non-availability of data with the necessary geographic detail, especially in developing countries. Accordingly, a recently completed World Bank project on estimating climate change impacts on Indian agriculture, has been the first major beneficiary of this database.

As such, the detailed cross-sectional and time series nature of the data set makes it suitable for any comprehensive study on Indian agriculture. We hope that potential researchers and policy analysts will utilize the extensive information in this data set to the fullest.

We now describe the economic, climatological and edaphic variables included in the database and their sources in detail.

 

VARIABLES IN THE DATA SET

 

The original data set was created by McKinsey and Robert Evenson (Growth Center at Yale University) between 1980 and 1990 and has been used in numerous studies of production and productivity in Indian agriculture. The data set contains observations for each of the variables for the agricultural years 1957/58 through 1986/87. The agricultural year 1957/58 is denoted by 1957. Unless noted, all variables are expressed as annual flows or average annual stocks or average annual levels.

The following sections describe in detail the economic, climate and edaphic variables used in the study. Over the course of the project, various errors were discovered in the original data set and corrected for. Some of the errors were huge and could substantially modify the results. For example, the price of sugar in 1980 is entered as Rs.71693.96, whereas the actual price is Rs. 380.86. Various such errors were discovered and amended. Other errors include mistakes in appropriate transformation of units. For example, for 1984 onwards, cotton production is reported in /000 bales but the price of cotton is reported in Rs./ton. In most cases, the errors were rectified by tallying them with the original census variables. In a small number of cases, values were approximated by using information from neighboring years or districts.

 

 

DATA SOURCE ABBREVIATIONS

 

API: Agricultural Prices in India.

APPCI: Area and Production of Principal Crops in India.

ASI: Agricultural Situation in India.

AWI: Agricultural Wages in India.

 

CSRS: Crop and Season Reports of the various States.

CTOI: Climatological Tables of Observatories in India.

 

DES: Directorate of Economics and Statistics.

 

FAI: Fertilizer Association of India.

FHPPI: Farm Harvest Prices of Principal crops in India.

FS: Fertilizer Statistics (published by FAI).

 

GOI: Government of India.

 

LS: Livestock Census.

 

MOA: Ministry of Agriculture.

 

PSA: Primary Census Abstract (reprinted in SAS).

 

SAS: Statistical Abstracts of India.

 

 

 

DEFINITIONS OF ECONOMIC VARIABLES

 

(1) COVERAGE:

The data set covers 271 districts within thirteen states of India. Kerala and Assam are the major agricultural states absent from the data set. Also absent, but less important agriculturally, are the minor states and Union Territories in the Northeastern part of India, as well as the far-northern states of Himachal Pradesh and Jammu & Kashmir.

The 271 districts and 13 states constitute the three primary northern wheat and northern rice producing states (Haryana, Punjab, Uttar Pradesh), two northwestern bajra-producing states (Gujarat and Rajasthan), three Eastern states (Bihar, Orissa and West Bengal) and the Semi-Arid Tropics States as specified by ICRISAT (namely Andhra Pradesh, Tamil Nadu, Karnataka, Maharashtra and Madhya Pradesh).

Any changes occurring in district boundaries have been accounted for. Original district boundaries have been preserved by consolidating new districts into their ‘parent’ districts (which is why the actual number of modern-day districts is considerably larger than 271). These changes have occurred, for example, because of the division of the former Punjab into Punjab, Haryana and Himachal Pradesh; the division of some districts into two or more smaller districts in many states (especially in Bihar); or the transfer of parts of one district to another. Using district latitudes and longitudes, the distance of a district’s center to the nearest shoreline was calculated by using Geographical Information Systems software (MAPINFO). The mean altitude of the geographic center of districts and weather stations is also calculated using State level Physical Maps and Climatological Tables from the National Center for Atmospheric Research (NCAR), USA, the National Climatic Data Center (NCDC), USA, the Blacklands Research Center, USA, FAO and the Indian Meteorological Departments (IMD) Pune.

 

(2) OUTPUTS:

x= major crops: BAJRA, JOWAR, MAIZE, RICE, WHEAT

y=minor crops: BAR (barley), COTN (cotton), GNUT (groundnut), GRAM, JUTE,

OPULS (other pulses), POTAT (potato), RAGI, TUR, RMSEED

(rapeseed and mustard), SESA (sesame), SOY (soybean), SUGAR (sugarcane), SUNFL (sun flower), TOBAC (tobacco)

Ax, Ay=Area Planted (‘000 ha)

Qx, Qy=Production (‘000 tons)

Px,Py=Farm Harvest Price (Rs/quintal)

HYVx=Area planted to HYV of crop x (‘000 ha)

 

 

(3) VARIABLE INPUTS:

AGLABOR=Number of rural males whose primary job classification is agricultural labor

CULTIVAT=Number of rural males whose primary job classification is cultivation (note: both AGLABOR and CULTIVAT are stock variables).

QLABOR=Wtd. sum of AGLABOR and CULTIVAT=(AGLABOR+CULTIVAT)*(# ofdays worked in the state by farm workers)

WAGE=Wtd. (by month) annual labor cost. Wages of a male ploughman were recorded; if not available then wages of a male field laborer or male "Other Agricultural Labor" were selected. June and August were weighted more heavily than other months because of the high intensity of field work during those months.

NITRO-TP; P205-TP; K20-TP=Prices of fertilizers (nitrogen, phosphorus and potassium) in Rs/ton. Prices of fertilizers are strictly controlled by the GOI, so the only cross-section price variation arises from the cost of transportation from the railhead to the field. Prices are based on reported maximum sale prices of common fertilizer compounds adjusted for the proportion of the nutrient present in each compound.

QNITRO, QP2O5, QK2O= Qantitites of fertilizers (nitorgen, phosphorus and potassium) in tons.

QBULLOCK=Number of castrated (male) cattle over the age of 3 years which are used in rural areas for work only.

QTRACTOR=Number of four-wheel machines (not tracked or walk behind two-wheeled ones).

PBULLOCK=(0.5)*(bullock price). The 0.5 represents the substantial annual flow of expenses entailed in breeding, raising and feeding bullocks, as well as the necessary rate of return on their ownership.

PTRACTOR=Average tractor prices (controlling for depreciation) using the prices of Eicher 24-HP tractors and Escort tractors.

 

(4) OTHER VARIABLES:

LITERACY= Proportion of rural males who are classified as literate (defined as "the ability to read and write in any language"). Census enumerators, beginning with the 1971 census, were required to observe each individual's ability to read and write before classifying him or her to be literate. As is true for all census variables, values for the inter-censal years were obtained by linear interpolation. Literacy rates change so slowly and so regularly that this procedure seems amply justified.

POPDEN= Population Density was calculated by dividing the population as per the population censuses by the area in each district. Like the LITERACY variable, values for the inter-censal years were linearly interpolated.

 

TABLE 1: SUMMARY OF ECONOMIC VARIABLES IN THE DATA SET

 

VARIABLES

SOURCE(S)

AVAILABLE BY

 

INTERPOLATED/

CONSTRUCTED

 

 

MONTH

YEAR

DISTRICT

STATE

 

(1) COVERAGE

 

 

 

 

 

 

STATE

-

-

-

-

-

-

DISTRICT

-

-

-

-

-

-

YEAR

-

-

-

-

-

-

(2) OUTPUTS

 

 

 

 

 

 

Ax, Ay (‘000 ha)

x=major crops

y=minor crops

APPCI (‘54-’70);

SAS;

CSRS;

ASI(‘70s-’80s)

NO

YES

YES

YES

NO

Qx, Qy (‘000 tons)

"

"

"

YES

YES

NO

Px, Py (Rs/quintal)

FHPPI (DES)

"

"

YES

YES

NO

HYVx (‘000 ha)

"

"

"

YES

YES

NO

(3) VARIABLE INPUTS

 

 

 

 

 

 

RURPOP

PSA;

SAS;

Census (‘51, ‘61, ‘71, ‘81)

NO

YES

YES

YES

INTER.

AGLABOR

"

"

"

YES

YES

INTER.

CULTIVAT

"

"

"

YES

YES

INTER.

QLABOR

"

"

"

?

YES

CONST.

WAGE

AWI (DES)

YES

"

YES

YES

NO

NITRO_TP

P205_TP (Rs/ton)

K20_TP

FS

"

"

YES

YES

CONST.

 

 

TABLE 1(a): SUMMARY OF ECONOMIC VARIABLES IN THE DATA SET (contd.)

 

VARIABLES

SOURCE(S)

AVAILABLE BY

 

INTERPOLATED/

CONSTRUCTED

 

 

MONTH

YEAR

DISTRICT

STATE

 

QBULLOCK

LS (1&2)

(‘56, ‘61, ‘66, ‘72, ‘77);

 

"

"

YES

YES

INTER.

QTRACTOR

"

"

"

YES

YES

INTER.

PBULLOCK

API (DES)

"

"

YES

YES

CONST.

PTRACTOR

"

"

"

YES

YES

CONST.

(4) OTHER INPUTS

 

 

 

 

 

 

Literacy

PSA;

SAS;

Census (‘51, ‘61, ‘71, ‘81)

NO

YES

YES

YES

INTER.

Population Density

PSA;

SAS;

Census (‘51, ‘61, ‘71, ’81)

NO

YES

YES

YES

INTER.

(Note: "Intermediate" variables have been omitted from this table).

 

CLIMATE VARIABLES

Climate variables are made available from the Food and Agricultural Organization (FAO). The climate variables come from 160 weather stations well scattered across India. The climate variables are 30 year norms (1931-1960). For information on location of weather stations see Table 3.

 

To extrapolate climate data from station to district level, a spatial statistical analysis is used which examines the determinants of the climate of each district. The procedure is based on the assumption that all the meteorological stations within a certain radius from the geographic center of the district contain useful information about that district’s climate. The choice of radius is made so that as many stations as possible are contained within the radius so that the estimates do not depend too heavily on any one station. A climate surface in the vicinity of the district is then estimated by running a weighted regression across all meteorological stations within the radius. Stations closer to the district center presumably contain more information than the stations far away. Hence the inverse of the square root of a station’s distance from the districts center is used as the appropriate weight in the regressions. Separate regressions are run for each district, as each district’s set of meteorological stations and the corresponding distances would be different. The dependent variables are monthly temperature and precipitation, and the independent variables include latitude, longitude, altitude and the distance from the nearest shoreline. As with the districts, the distance to sea of these weather stations was calculated by using Geographical Information Systems Software (MAPINFO) for India.

District-wise estimated normal monthly rainfall and temperature for all the twelve months are provided in the data set. The monthly rainfall and temperatures are measured in mm and oC, respectively. Rainfall and temperature are referred as RNMON and TNMON, where MON is the abbreviation for the month name.

 

 

 

EDAPHIC VARIABLES

 

Though a number of organizations such as The National Bureau of Soil Survey and Land Use Planning, Nagpur (India), collect and compile soil data, there are no district-level soil data sets covering the entire country available in India as of now. McKinsey has carefully compiled the soil data from various sources and maps and prepared one aggregate soil data set which is described below. The data set includes 19 dummy variables [variables names DMS02 through DMS20] specifying soil type. The value of a particular dummy variable in a given district equals one if that dummy’s soil type is one of the two predominant soil types in the district; that is, if that soil type covers the largest, or second-largest, amount of area in the district.

 

The soil type dummy variables are a rich source of information, and their estimated coefficients are usually significantly different from zero in most regressions: the variables, as expected, help greatly to explain net revenue [based, no doubt, on their contribution to crop output].

 

Two caveats, however, are in order, one concerning the construction of the dummy variables and the other concerning their interpretations. First, most districts contain more than two soil types: the soil maps display four or even five types for many districts. And the third-most prevalent soil type in some districts may cover more area than the second-most prevalent type in other districts. Second, because two of the dummy variables in each district can have the value of one the coefficients of the dummy variables cannot be interpreted as usual.

 

 

A. SOIL TYPE

1. Variable name: DMS02--DMS20

where DM denotes a dummy variable and S02--S20 represent the following soil types:

 

a. 01 not used

b. 02 Laterite

c. 03 Red and Yellow

d. 04 Shallow Black

e. 05 Medium Black

f. 06 Deep Black

g. 07 Mixed Red and Black

h. 08 Coastal Alluvial

i. 09 Deltaic Alluvium

j. 10 Calcerous

k. 11 Gray Brown

l. 12 Desert

m. 13 Tarai

n. 14 Black (Karail)

o. 15 Saline and Alkaline

p. 16 Alluvial River

q. 17 Skeletal

r. 18 Saline and Deltaic

s. 19 Red

t. 20 Red and Gravely

 

Source(s): Visual inspection of soil maps for each State: found in S. P. Raychaudhuri et al, Soils of India (New Delhi: Indian Council of Agricultural Research, 1963)

 

B. STORIE INDEX

1. Variable name

a. STRA

· measuring the character of the soil profile

· values range from 0.65 to 1.00, where a higher value represents a more favorable or more productive rating.

b. STRB

· measuring topography, texture and structure

· values range from 0.65 to 1.00, where a higher value represents a more favorable or more productive rating.

c. STRC

· measuring the degree of climatic suitability, salinity, stoniness and the tendency to erode

· values range from 0.65 to 1.00, where a higher value represents a more favorable or more productive rating.

d. STORIE

· product of STRA, STRB and STRC

· thus the values of STORIE could range from as low as 0.274625 to 1.00. 1) 0.274625 = 0.653 and thus is the theoretical minimum 2) the actual minimum value in any district is 0.

· the combined Storie index is designed as an overall measure of soil productivity

2. source

a. K. B. Shome and S. P. Raychaudhuri, Rating of Soils of India Proceedings,

b. National Institute of Sciences of India, vol. 26, Part A, 1960

c. method adapted from R. E. Storie, Transactions, Fourth International

d. Conference of Soil Science, 1950

 

C. SOIL FERTILITY STATUS

1. variable names and components

a. N

i. nitrogen fertility level

ii. values include 1 for low, 2 for medium and 3 for high

b. P

i. phosphorous fertility level

ii. values include 1 for low, 2 for medium and 3 for high

c. K

i. potassium fertility level

ii. values include 1 for low, 2 for medium and 3 for high

d. twelve fertility class groups

i. dummy variables

ii. each involves a combination of fertility level of N, P and K

iii. the groups and the levels of N, P and K, are listed in the following table:

 

 

 

 

 

TABLE 2: Soil dummy variables

 

Variable

N Level

P Level

K Level

DMF01

Low

Low

Low

DMF02

Low

Low

High

DMF03

Low

Low

Medium

DMF04

Low

Medium

Low

DMF05

Low

Medium

High

DMF06

Low

Medium

Medium

DMF07

Medium

Low

Low

DMF08

Medium

Low

Medium

DMF09

Medium

Low

High

DMF10

Medium

Medium

Low

DMF11

Medium

Medium

Medium

DMF12

Medium

Medium

High

 

2. Source

a. originally compiled by A. B. Ghosh and Rehanul Hasan, Indian Agricultural Research Institute, New Delhi, based on the results of soil tests carried out and data provided by, state and regional soil testing laboratories

b. data presented by M. Velayuthan and A. B. Ghosh, Proceedings, Fertilizer Association of India National Seminar on Strategies for Achieving Fertilizer Consumption Targets and Improving Fertilizer Use Efficiency, 1981

c. published in various annual editions of Fertilizer Statistics of India, published by the Fertilizer Association of India

 

D. SOIL PH

1. source: National Atlas of India, vol. 1, plate 59

2. Variables:

a. a series of dummy variables

DMPH5: strongly alkali 4.5<pH<5.5

DMPH6: slightly alkali 5.5<pH<6.5

DMPH7: neutral 6.5<pH<7.5

DMPH8: slightly acid 7.5<pH<8.5

DMPH9: strongly acid 8.5<pH<9.5

b. single variable PH# whose value is PH reading from 5 to 9

 

 

E. AQUIFERS

1. source: National Atlas of India, vol. 1, several plates

a. plate 87: All-India

b. plate 88: Northern India

c. plate 89: Western India

d. plate 90: Central India

e. plate 91: Eastern India

f. plate 92: Southern India

 

2. Variables:

DMAQ1: dummy variable = 1 if aquifer is <100 meters thick

DMAQ2: dummy variable = 1 if aquifer is 100 - 150 meters thick

DMAQ3: dummy variable = 1 if aquifer is > 150 meters thick

 

F. TOPSOIL DEPTH

1. Source: National Atlas of India, vol. 1, Plate 50: "Depth of Soil, All-India"

2. Variables:

a. DMTS1: dummy variable = 1 if topsoil is 0 - 25 cm. thick

b. DMTS2: dummy variable = 1 if topsoil is 25-50 cm. thick

c. DMTS3: dummy variable = 1 if topsoil is 50 - 100 cm. thick

d. DMTS4: dummy variable = 1 if topsoil is 100 - 300cm. thick

e. DMTS5: dummy variable = 1 if topsoil is > 300 cm. Thick

 

LEGEND OF THE DATA BASE

 

dictionary

names=y

separator=,

mustsurround=n

surroundchar="

quotechar='

extension=dat

missing=.

numeric=n

fixed=n

dictionary=dct

date=mm/dd/yyyy

variables

1 12 R YEAR

14 12 R AGGDPDF

27 12 R CODE

40 12 R POPDEN

53 12 R PRSEED

66 12 R HYVWHEAT

79 12 R HYVRICE

92 12 R HYVMAIZE

105 12 R HYVBAJRA

118 12 R HYVJOWAR

131 12 R AGLABOR

144 12 R CULTIVAT

157 12 R WAGE

170 12 R NCA

183 12 R GCA

196 12 R NIA

209 12 R GIA

222 12 R YWHEAT

235 12 R YRICE

248 12 R YSUGAR

261 12 R YMAIZE

274 12 R YPOTATO

287 12 R YGNUT

300 12 R YBARLEY

313 12 R YTOBAC

326 12 R YGRAM

339 12 R YTUR

352 12 R YRAGI

365 12 R YSESAMUM

378 12 R YRMSEED

391 12 R YBAJRA

404 12 R YCOTTON

417 12 R YJOWAR

430 12 R YOPULS

443 12 R YJUTE

456 12 R YSOY

469 12 R YSUNFLWR

482 12 R ROADS

495 12 R LITERACY

508 12 R FEBXT

521 12 R FEBNT

534 12 R DMS01

547 12 R DMS02

560 12 R DMS03

573 12 R DMS04

586 12 R DMS05

599 12 R DMS06

612 12 R DMS07

625 12 R DMS08

638 12 R DMS09

651 12 R DMS10

664 12 R DMS11

677 12 R DMS12

690 12 R DMS13

703 12 R DMS14

716 12 R DMS15

729 12 R DMS16

742 12 R DMS17

755 12 R DMS18

768 12 R DMS19

781 12 R DMS20

794 12 R DMS21

807 12 R DMAQ3

820 12 R DMAQ2

833 12 R DMAQ1

846 12 R DMSLP4

859 12 R DMSLP567

872 12 R DMPH4

885 12 R DMPH5

898 12 R DMPH6

911 12 R DMPH7

924 12 R DMPH8

937 12 R DMSLP1

950 12 R DMSLP2

963 12 R DMSLP3

976 12 R COSTAGLB

989 12 R COSTCULT

1002 12 R COSTBULL

1015 12 R COSTTRAC

1028 12 R COSTNITR

1041 12 R COSTP2O5

1054 12 R COSTK2O

1067 12 R DMTS1

1080 12 R DMTS2

1093 12 R DMTS3

1106 12 R DMTS4

1119 12 R DMTS5

1132 12 R QSUGAR

1145 12 R DAYS

1158 12 R STATE

1171 12 R DISTRICT

1184 12 R QBULLOCK

1197 12 R QTRACTOR

1210 12 R PTRACTOR

1223 12 R PUPBULL

1236 12 R PBULLOCK

1249 12 R QLABOR

1262 12 R QNITRO

1275 12 R QP2O5

1288 12 R QK2O

1301 12 R PNITRO

1314 12 R PP2O5

1327 12 R PK2O

1340 12 R QLAND

1353 12 R ROPUMP

1366 12 R RPWPUMP

1379 12 R UOPUMP

1392 12 R UEPUMP

1405 12 R UPWPUMP

1418 12 R PHYVWHT

1431 12 R PHYVRICE

1444 12 R PHYVJOWR

1457 12 R PHYVBAJR

1470 12 R PHYVMAIZ

1483 12 R IROADS

1496 12 R AWHEAT

1509 12 R QWHEAT

1522 12 R ARICE

1535 12 R QRICE

1548 12 R ASUGAR

1561 12 R AMAIZE

1574 12 R QMAIZE

1587 12 R APOTATO

1600 12 R QPOTATO

1613 12 R AGNUT

1626 12 R QGNUT

1639 12 R ABARLEY

1652 12 R QBARLEY

1665 12 R ATOBAC

1678 12 R QTOBAC

1691 12 R AGRAM

1704 12 R QGRAM

1717 12 R ATUR

1730 12 R QTUR

1743 12 R ARAGI

1756 12 R QRAGI

1769 12 R ASESAMUM

1782 12 R QSESAMUM

1795 12 R ARMSEED

1808 12 R QRMSEED

1821 12 R PWHEAT

1834 12 R PRICE

1847 12 R PSUGAR

1860 12 R PMAIZE

1873 12 R PPOTATO

1886 12 R PGNUT

1899 12 R PBARLEY

1912 12 R PTOBAC

1925 12 R PGRAM

1938 12 R PTUR

1951 12 R PRAGI

1964 12 R PSESAMUM

1977 12 R PRMSEED

1990 12 R ABAJRA

2003 12 R QBAJRA

2016 12 R ACOTTON

2029 12 R QCOTTON

2042 12 R AJOWAR

2055 12 R QJOWAR

2068 12 R PJOWAR

2081 12 R PBAJRA

2094 12 R PCOTTON

2107 12 R AOPULS

2120 12 R QOPULS

2133 12 R POPULS

2146 12 R AJUTE

2159 12 R QJUTE

2172 12 R PJUTE

2185 12 R ASOY

2198 12 R QSOY

2211 12 R ASUNFLWR

2224 12 R QSUNFLWR

2237 12 R PSOY

2250 12 R PSUNFLWR

2263 30 c DISTNAME

2294 12 R ALT

2307 29 c STATENAM

2337 12 R DSEA

2350 12 R LON

2363 12 R LAT

2376 12 R RNJAN

2389 12 R RNFEB

2402 12 R RNMAR

2415 12 R RNAPR

2428 12 R RNMAY

2441 12 R RNJUN

2454 12 R RNJUL

2467 12 R RNAUG

2480 12 R RNSEP

2493 12 R RNOCT

2506 12 R RNNOV

2519 12 R RNDEC

2532 12 R TNJAN

2545 12 R TNFEB

2558 12 R TNMAR

2571 12 R TNAPR

2584 12 R TNMAY

2597 12 R TNJUN

2610 12 R TNJUL

2623 12 R TNAUG

2636 12 R TNSEP

2649 12 R TNOCT

2662 12 R TNNOV

2675 12 R TNDEC

2688 12 R DMSTS1

2701 12 R DMSTS2

2714 12 R DMSTS3

2727 12 R DMSTS4

2740 12 R DMSTS5

2753 12 R TOTAREA

2766 12 R AHYV

2779 12 R QBULLHA

2792 12 R QTRACHA

2805 12 R DMXS01

2818 12 R DMSX01

2831 12 R DMXS02

2844 12 R DMSX02

2857 12 R DMXS03

2870 12 R DMXS04

2883 12 R DMXS05

2896 12 R DMXS06

2909 12 R DMXS07

2922 12 R DMXTS1

2935 12 R DMSXT1

2948 12 R DMXTS2

2961 12 R DMXTS3

endvars

 

Table 3: List of weather stations

 

METSTN

DISTRICT

STATE/UNION_TERRITORY

ALTITUDE

(m)

LATITUDE (o,/)

LONGITUDE

(o,/)

AGRA

AGRA

UTTAR_PRADESH

169

27.1

78.02

AHMADABAD

AHMADABAD

GUJARAT

55

23.04

72.38

AHMADNAGAR

AHMADNAGAR

MAHARASHTRA

657

19.05

74.48

AJMER

AJMER

RAJASTHAN

486

26.27

74.37

AKOLA

AKOLA

MAHARASHTRA

282

20.42

77.02

ALIBAG

RAIGARH

MAHARASHTRA

7

18.38

72.52

ALIGARH

ALIGARH

UTTAR_PRADESH

187

27.53

78.04

ALLABABAD

ALLAHABAD

UTTAR_PRADESH

98

25.27

81.44

AMBALA

AMBALA

HARYANA

272

30.23

76.46

AMINI

(N/A)

(Island)

4

11.07

72.44

AMRAOTI

AMRAVATI

MAHARASHTRA

370

20.56

77.47

AMRITSAR

AMRITSAR

PUNJAB

234

31.38

74.52

ANGUL

DHENKANAL

ORISSA

139

20.5

85.06

ASANSOL

BARDDHAMAN

WEST_BENGAL

126

23.41

86.58

AURANGABAD

AURANGABAD

MAHARASHTRA

581

19.53

75.2

BAHRAICH

BAHRAICH

UTTAR_PRADESH

124

27.34

81.36

BALASORE

BALESHWAR

ORISSA

20

21.31

86.56

BALEHONNUR

CHIKMAGALUR

KARNATAKA

889

13.22

75.27

BANGALORE

BANGLORE

KARNATAKA

921

12.58

77.35

BAREILLY

BAREILLY

UTTAR_PRADESH

173

28.22

79.24

BARMER

BARMER

RAJASTHAN

194

25.45

71.23

BARODA

VADODARA

GUJARAT

34

22.18

73.15

BELGAUM

BELGAUM

KARNATAKA

753

15.51

74.32

BELLARY

BELLARY

KARNATAKA

449

15.09

76.51

BERHAMPORE

MURSHIDABAD

WEST_BENGAL

19

24.08

88.16

BHAUNAGAR (AERO)

BHAVNAGAR

GUJARAT

11

21.45

72.11

BHOPAL (BAIRAGARH)

SEHORE

MADHYA_PRADESH

523

23.17

77.21

BHUJ (RUDRAMATA)

KACHCHH

GUJARAT

80

23.15

69.4

BIDAR

BIDAR

KARNATAKA

664

17.55

77.32

BIJAPUR

BIJAPUR

KARNATAKA

594

16.49

75.43

BIKANER

BIKANER

RAJASTHAN

224

28

73.18

BOMBAY

GREATER BOMBAY

MAHARASHTRA

11

18.54

72.49

BURDWAN

BARDDHAMAN

WEST_BENGAL

32

23.14

87.51

CALCUTTA (DUM DUM)

24_PARGANAS

WEST_BENGAL

6

22.39

88.26

CHAIBASA

SINGHBHUM

BIHAR

226

22.33

85.49

CHANDBALI

BALESHWAR

ORISSA

6

20.47

86.44

CHANDRAPUR

CHANDRAPUR

MAHARASHTRA

193

19.58

79.18

CHERRAPUNJI

(N/A)

ASSAM

1313

25.15

91.44

CHITRADURGA

CHITRADURGA

KARNATAKA

733

14.14

76.26

COCHIN

ERNAKULAM

KERELA

3

9.58

76.14

COIMBATORE

COIMBATORE

TAMIL_NADU

409

11

78.58

COONOOR

NILGIRI

TAMIL_NADU

1747

11.21

76.48

CUDDALORE

SOUTH_ARCOT

TAMIL_NADU

12

11.46

79.46

CUDDAPAH

CUDDAPAH

ANDHRA_PRADESH

130

14.29

78.5

CUTTACK

CUTTACK

ORISSA

27

20.28

85.56

 

Table 3: List of weather stations (contd.)

 

METSTN

DISTRICT

STATE / UNION_TERRITORY

ALTITUDE (m)

LATITUDE (o,/)

LONGITUDE (o,/)

DALTONGANJ

PALAMU

BIHAR

221

24.03

84.04

DARBHANGA

DARBHANGA

BIHAR

49

26.1

85.54

DARJEELING

DARJILING

WEST_BENGAL

2127

27.03

88.16

DEHRA DUN

DEHRADUN

UTTAR_PRADESH

682

30.19

78.02

DHAMBAD

DHANBAD

BIHAR

257

23.47

86.26

DHUBRI

(N/A)

ASSAM

35

26.01

89.58

DIBRUGARH

(N/A)

ASSAM

110

27.29

95.01

DOHAD

PANCH-MAHALS

GUJARAT

333

22.5

74.16

DUMKA

DUMKA

BIHAR

149

24.16

87.15

DWARKA

JAMNAGAR

GUJARAT

11

22.22

69.05

FATEHPOR

FATEHPUR

UTTAR_PRADESH

114

25.56

80.5

GADAG

DHARWAD

KARNATAKA

650

15.25

75.38

GANGANAGAR

GANGANAGAR

RAJASTHAN

177

29.55

73.53

GAUHATI

(N/A)

ASSAM

54

26.06

91.35

GAYA

GAYA

BIHAR

116

24.45

84.57

GONDA

GONDA

UTTAR_PRADESH

110

27.08

81.58

GOPALPUR

GANJAM

ORISSA

17

19.16

84.53

GORAKHPUR

GORAKHPUR

UTTAR_PRADESH

77

26.45

83.22

GULBARGA

GULBARGA

KARNATAKA

458

17.21

76.51

GUNA

GUNA

MADHYA_PRADESH

478

24.39

77.19

GWALIOR

GWALIOR

MADHYA_PRADESH

207

26.14

78.15

HANAMKONDA

ADILABAD

ANDHRA_PRADESH

269

19.01

79.34

HASSAN

HASSAN

KARNATAKA

960

13

76.09

HAZARIBAGH

HAZARIBAG

BIHAR

611

23.59

85.22

HISSAR

HISSAR

HARYANA

221

29.1

75.44

HONAVAR

UTTAR_KANNAD

KARNATAKA

29

14.17

74.27

HOSHANGABAD

SEHORE

MADHYA_PRADESH

302

22.46

77.46

HYDERABAD

HYDERABAD

ANDHRA_PRADESH

545

17.27

78.28

INDORE

INDORE

MADHYA_PRADESH

567

22.43

75.48

JABALPUR

JABALPUR

MADHYA_PRADESH

393

23.12

79.57

JAGDALPUR

BASTAR

MADHYA_PRADESH

553

19.05

82.02

JAIPUR (SANGANER)

JAIPUR

RAJASTHAN

390

26.49

75.48

JALGAON

JALGAON

MAHARASHTRA

201

21.03

75.34

JALPAIGURI

JALPAIGURI

WEST_BENGAL

83

26.32

88.43

JAMMU

(N/A)

JAMMU & KASHMIR

366

32.4

74.5

JAMNAGAR (AERO)

JAMNAGAR

GUJARAT

20

22.27

70.02

JAMSHEDPUR

SINGHBHUM

BIHAR

129

22.49

86.11

JHALAWAR

JHALAWAR

RAJASTHAN

321

24.32

76.1

JHANSI

JHANSI

UTTAR_PRADESH

251

25.27

78.35

JODHPUR

JODHPUR

RAJASTHAN

224

26.18

73.01

KAKINADA

EAST_GODAVARI

ANDHRA_PRADESH

8

16.57

82.14

KALIMPONG

DARJILING

WEST_BENGAL

1209

27.04

88.28

KALINGAPATAM

SRIKAKULAM

ANDHRA_PRADESH

6

18.2

84.08

KANKER

BASTAR

MADHYA_PRADESH

402

20.16

81.29

KANPUR AIR FLD

KANPUR

UTTAR_PRADESH

126

26.26

80.22

 

Table 3: List of weather stations (contd.)

 

METSTN

DISTRICT

STATE / UNION_TERRITORY

ALTITUDE (m)

LATITUDE (o,/)

LONGITUDE (o,/)

KHANDWA

EAST_NIMAR

MADHYA_PRADESH

318

21.5

76.22

 

KODAIKANAL

MADURAI

TAMIL_NADU

2343

10.14

77.28

KOTA

KOTA

RAJASTHAN

257

25.11

75.51

KOZHIKODE (CALICUT)

(N/A)

KERELA

5

11.15

75.47

KRISHNANAGAR

NADIA

WEST_BENGAL

15

23.24

88.31

KURNOOL

KURNOOL

ANDHRA_PRADESH

281

15.5

78.04

LEH

(N/A)

JAMMU & KASHMIR

3514

34.09

77.34

LUCKNOW

LUCKNOW

UTTAR_PRADESH

111

26.52

80.56

LUDHIANA

LUDHIANA

PUNJAB

247

30.56

75.52

LUMDING

(N/A)

ASSAM

149

25.45

93.11

MACHILIPATAM

KRISHNA

ANDHRA_PRADESH

3

16.12

81.09

MADRAS (MINAMBAKKAM)

CHENGALPATTU

TAMIL_NADU

16

13

80.11

MADURAI

MADURAI

TAMIL_NADU

133

9.55

78.07

MAHABALESHWAR

SATARA

MAHARASHTRA

1382

17.56

73.4

MAINPURI

MAINPURI

UTTAR_PRADESH

157

27.14

79.03

MALDA

MALDAH

WEST_BENGAL

31

25.02

88.08

MALEGAON

NASHIK

MAHARASHTRA

437

20.33

74.32

MANGALORE

DAKSHIN_KANNAD

KARNATAKA

22

12.52

74.51

MARMUGAO

NORTH_GOA

GOA

62

15.25

73.47

MERCARA

KODAGU

KARNATAKA

1152

12.25

75.44

MIDNAPORE

MEDINIPUR

WEST_BENGAL

45

22.25

87.19

MINICOY

(N/A)

(Island)

2

8.18

73

MIRAJ (SANGLI)

SANGLI

MAHARASHTRA

554

16.49

74.41

MOTIHARI

CHAMPARAN

BIHAR

66

26.4

84.55

MOUNT ABU

SIROHI

RAJASTHAN

1195

24.36

72.43

MOWGONG

HAMIRPUR

UTTAR_PRADESH

229

25.04

79.27

MUKTESWAR (KUMAON)

ALMORA

UTTAR_PRADESH

2311

29.28

79.39

MUSSOORIE

TEHRI_GARWAHAL

UTTAR_PRADESH

2042

30.27

78.05

MYSORE

MANDYA

KARNATAKA

767

12.18

76.42

NAGAPPATTINAM

THANJAVUR

TAMIL_NADU

9

10.46

79.51

NELLORE

NELLORE

ANDHRA_PRADESH

20

14.27

79.59

NEW DELHI-SAFDARJANG

(N/A)

NEW_DELHI

216

28.35

77.12

NIMACH

MANDSAUR

MADHYA_PRADESH

496

24.28

74.54

NIZAMABAD

NIZAMABAD

ANDHRA_PRADESH

381

18.4

78.06

PACHMARHI

HOSHANGABAD

MADHYA_PRADESH

1075

22.28

78.26

PAMBAN

(N/A)

(Island)

11

9.16

79.18

PATNA

PATNA

BIHAR

53

25.37

85.1

PENDRA

BILASPUR

MADHYA_PRADESH

625

22.46

81.54

PHALODI

JODHPUR

RAJASTHAN

234

27.08

72.22

POONA

PUNE

MAHARASHTRA

559

18.32

73.51

PURI

PURI

ORISSA

6

19.48

85.49

PURNEA

BHAGALPUR

BIHAR

38

25.16

87.28

RAICHOR

RAICHOR

KARNATAKA

400

16.12

77.21

RAIPUR

RAIPUR

MADHYA_PRADESH

298

21.14

81.39

 

 

Table 3: List of weather stations (contd.)

 

METSTN

DISTRICT

STATE / UNION_TERRITORY

ALTITUDE (m)

LATITUDE (o,/)

LONGITUDE (o,/)

RAJKOT

RAJKOT

GUJARAT

138

22.18

70.47

RANCHI

RANCHI

BIHAR

655

23.26

85.24

RENTACHINTALA

GUNTUR

ANDHRA_PRADESH

106

16.33

79.33

ROORKEE

SAHARANPUR

UTTAR_PRADESH

274

29.51

77.53

SABAUR

BHAGALPUR

BIHAR

37

25.14

87.04

SAGAR

SAGAR

MADHYA_PRADESH

551

23.51

78.45

SAGAR ISLAND

(N/A)

(Island)

3

21.39

88.03

SALEM

SALEM

TAMIL_NADU

278

11.39

78.1

SAMBALPUR

SAMBALPUR

ORISSA

148

21.28

83.58

SATNA

SATNA

MADHYA_PRADESH

317

24.34

80.5

SEONI

SEONI

MADHYA_PRADESH

619

22.05

79.33

SHILLONG

(N/A)

MEGHALAYA

1598

25.34

91.53

SHOLAPUR

SOLAPUR

MAHARASHTRA

479

17.4

75.54

SIBSAGAR

(N/A)

ASSAM

97

26.59

94.38

SILCHAR

(N/A)

ASSAM

29

24.49

92.48

SIMLA

(N/A)

HIMACHAL_PRADESH

2202

31.06

77.1

SRINAGAR

(N/A)

JAMMU & KASHMIR

1586

34.05

74.5

SURAT

SURAT

GUJARAT

12

21.12

72.5

TEZPUR

(N/A)

ASSAM

79

26.37

92.47

TIRUCHCHIRAPALLI

TIRUCHCHIRAPPALLI

TAMIL_NADU

88

10.46

78.43

TRIVANDRUM

THIRUVANENTHAPURAM

KERELA

64

8.29

76.57

UMARIA

SHAHDOL

MADHYA_PRADESH

459

23.32

80.53

VARANASI (BABATPUR)

VARANASI

UTTAR_PRADESH

85

25.27

82.52

VELLORE

NORTH_ARCOT_AMBEDKAR

TAMIL_NADU

214

12.55

79.09

VERAVAL

JUNAGARH

GUJARAT

8

20.54

70.22

VISHAKHAPATNAM

VISAKHAPATNAM

ANDHRA_PRADESH

3

17.43

83.14