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 productivity2. 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 |