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Journal of Agrometeorology 13 (!): 31-37 (June 2011)

Nakshatra based rainfall variability, trends and its influence on rice-wheat P._!:O_~~ctioir - A case st~~~ Jlver two sites i~ ·Bi~¥' India "'" -. ~

N. SUBASW, S.S. SINGH and NEHA PRIY A Division of Crop Research, !CAR Research Complex/or Eastern Region, JCAR Parisar . .

Bihar-Veterinary College P. 0., Patna 800014, Bihar

Email: n suby@rediffmail.com, n suby@sifY.com

ABSTRACT

An attempt has been made to study the variability and trends of rainfall during nakshatra periods for two sites, Patna and Samastipur of Bihar and verified the relation of four traditional knowledge/ proverbs/ beliefs of nakshatra based rainfall pattern, which are popular in this part of the region with rice-wheat productivity. Maximum rainfall and highest rainy days occurred during punarvasu nakshatra (July 6 - July 19) over both sites followed by pushya (July 20- Aug 2). The coefficient of variation of rainfall shows below 100 % from aridhra (June 22 - July 5) to uttara (Sept 13-26) at Patna and from rohini to purbha (Aug 31- Sept 12) for Samastipur. A significant increasing trend of 0.965 mm y·' and decreasing trend of -0.857 mm y·', respectively have been noticed during pubbha and hasta at Patna. However, a significant increasing trend of 1.536 mm y·1 and decreasing trend of--0.774 mm y-1, respectively have been noticed during aridhra and hasta at Samastipur.

Keywords: Nakshatra, Rainfall variability, Mann-Kendall non-parametric test, Productivity index, Rainfall anomaly index, Correlation '

There is considerable traditional/proverbial knowledge about the variability of rainfall patterns and.traditional rice­wheat productivity, since rainfed cultivat ion has been carried out for several centuries in this regiorrThe periods used by the farmer are h.owever, not weeks o1 months but so-called 'nakshatras' which are 13 or 14.days periods which are also based on the solar calendar (IMO, 1998) . . The nakshatras stand for the twenty seven constellations through which the sun passes in a year. Hence the period of each nakshatra is about 14 days. The nakshatra l:ommenc.es when the sun enters the specific constellation. · The knowledge of the variability is thus these timer units rath~rthan weeks or months (Gadgil and Rao, 2000). Golakia (1992) compiled proverbs from different communities for predicting 'the moods of monsoon over Saurashtra. Varshneya et a/(2002) developed nakshatra-varsha almanac for Maharashtra and this was designed for farmers to predict rainfall distribution in next rainy .season. The techniques gave prediction of onset and withdrawal of monsoon and distribution ofrainfall about six months in .advance. Based on pictorial . assistance, it is possible for the. farmers to change the crops to decide area under kharif and rabi season. It is found that there is relative

·advantage of this calendar for Maharashtra (Vaidya, 2004). Kanani (2006) tested traditional methods of weather forecasting in Gujarat using the participatory approach. They formed a group called "Varsha Vjgyan Mandat" including researchers and farmers of Junagadh and validated tradi~ional beliefs across the region. Thus, nakshatra based rainfall

periods have more influence on rice and wheat production and studies on variability and trends of ralnfall based on nakshatra periods and its influence on rice-wheat production are lacking. ln the present study, variability and trends of rainfall during naksh~tra periods for two sites viz., Patna and Samastipur, representfng different agro-climatological zones of Bihar have been analyzed. Some of the traditional/ proverbial knowledge based on nakshatra period rainfall and rice-wheat productivity is also verified over these two sites.

MATERIALSANDMETHODS

The daily rainfall data recorded at IARI regional station, Pusa, Samastipur; and Agricultural Research Institute, Patna during the period 1960-2008 were collected. The detailed methodology adopted for quality, completeness and homogeneity_was disc!-Jssed in detail Parthasarathy et al. ( 1993, l 995a, b ). These daily rainfall data were converted into nakshatra period rainfall and computed mean, standard deviation and coefficient of variation. 'fhe productivity of rainy season (kharif) rice over the selected sites were taken from the Directorate of Rice, Ministry of Agriculture, Govt. offndia ~nd are available on-line at http://www.dacnet.nic.in and the productivity of wheat were taken from the Directorate of Statistics and Evaluation, Govt. of Bihar.

Trend analysis

Mann-Kendall (MK) (Mann, 1945; Kendall, 1975) is a

' Present address: Project Directorate for Farming Systenis Research, Modipuram- 250110, Meerut. UP

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me201 1] Climate change adaptation strategies for agro-ecosystem 32

'able 1: Mean r.ainfall ~d ra_iny days and its standard c4<0ati9n (SD) and coefficient of.,yariatj?n (CVJ during nakshatnuie!iqd . · over Patna and-S~mlstipur districts ofBihar .-... ,, ~ . - . .-. -. <-"~ . - · - • ~

lakshtra PerWd Patna Ram fall

Mean SD CV {mm} (mm) (%)

shwini April 13 to April 26 6. 1 9. 1 148 harani April 27 to May I 0 13.5 fr7 161 ritika May 11 to24 21.3 30.2 142 ohini · May 25 to June 7 26.4 39.8 151 rigash ira June 8 to 21 60. l 64.6 108 ridhra June 22 to July 5 116.5 96.3 83 marvasu July 6 to July 19 · 116.0 l 10.3 63 1shya July 20 to Aug 2 13 1.0 98.9 76 :Iesha Aug 3 to Aug 16 115.8 95.0 82 akha Aug 17 to Aug 30 131.l 73.5 56 1rbha Aug 31 to Sep 12 90.3 67.8 75 tarn Sep 13 to Sep26 90.6 82.8 91 !Sta Sep 27 to Oct 9 76.4 85.3 112 1itta Oct 10 to Oct 23 19.9 45. I 226 'llthi Oct 24 to Nov 5 4.7 12.8 271 shalca Nov 6 to Nov 18 5.5 15.4 278 uradha Nov 19 to Dec 2 1.8 5. 1 289 :shta Dec 3 to Dec 15 0.9 4.2 482 iola Dec 16 to Dec 28 3.3 9.1 272 -vashada Dec 29 to Jan 10 3.3 5.9 181 arashada Jan 11 to Jan 23 4.9 8.0_ 161 . van a· Jan 24 to Feb 5 52 . 8.1 155 anishta Feb 6 to Feb 18 7.0 12.6 181 1tabhista Feb 19 to Mar 3 5.8 10.7 186 irva Bh2dra Mar4 to Mar 17 4.6 10.6 ' 231 ara Bhadra Mar rs to Mar 30 2.9 . 5.5 188 ' a ti Mar3 t to Aeril 12 1.5 '.?~ 266 ,,.

1parametric trend test basically involves the ranks lined by each data in the data series and is a statistical 'no type hypothesis testing procedure for the existence rer:ids and does not estimate the slope of trends. The :nitude of the trends was estimated using Seri Slope (Sen, 8) and according to Hirsch et al. ( 1982) Sen'~ method was JSt against extreme outliers. The. Mann-Kendal test :n.n ( 1945) and Kendall (1975)) ha

1s been used to detect

ds in hydrometeorological time series data.

ductivity variability

The production of rice depends on type of soil, seeds , crop area, availability of irrigation fac ilities, fertilizers, icides and also on the government incentives to the .ing sector during,a~~ar as well as on ·the meteorological meters such as rainfall, temperature, relative humidity solar energy. The non-meteorological parameters i. e., :!>ta! ·technological inputs to the fanning sector have gr\)wing steadily and are difficult to quantify. Therefore, .ow the pattern of trends and to qqantify the growth rate tal technological inputs to the agricultural sector the d productivity .was. fitted into a linear as well as any ·best fit (Subash et al., 2009; Subash and Ram Mohan, ).

To normalize the productivity, another index, the

Samastipur

Rain:z: da:z:s Rainfall Rainy days Mean .so CV Mean SD CV Mean SD CV (mm} (mm) (%) (mm} (mm} (%) \mm) (mm) (%) 0.6 0.9 152 10.1 14.8 146 0.8 1.2 141 0.9 1.0 I 17 21.l 23.6 112 1.4 1.3 87 1.2 1.3 113 31.1 35.6 114 1.6 1.5 95 1.4 1.4 94 36.8 34.9 95 2.0 1.6 78 2.7 2.2 80 76.2 54.2 71 3.5 2.2 62 4.6 2.2 47 128.5 86.8 68 5.0 2.2 44 6.5 2.7 41 159.6 99.1 62 6.6 2.6 40 5.9 2.5 43 139.l 112.3 81 6.1 2.5 41 5.6 2.4 43 132.4 110.5 84 5.6 3.0 54 5.8 2.1 36 135.6 116.2 86 5.4 2.4 44 4.6 2.3 51 106.0 88.9 84 5.1 2.J 45 4.0 2.1 51 104.2 105.8 102 4.2 2.1 49 2.8 2.0 74 77.8 IOJ.5 133 2.8 2.1 74 0.9 1.2 139 22 .. 0 41.6 189 0.9 1.2 129 0.3 0.8 240 4.8 12.6 265 0.3 0.8 224 0.3 0.8 249 3.7 10.5 286 0.3 0.7 253 0.2 0.5 292 3.7 10.5 286 0.0 0.0 0 0.1 0.3 '523 1.6 6.1 380 0.1 0.4 318 0.3 0.7 204 3.2 6.1 193 0.4 0.7 198 0.4 0.7 160 3.3 . 8.5 . 255 .. •. '0:3 0.6 191 0.5 0.7 147 4.0 6.5 160 0.4 0.6 151 0.5 0.9 160 6.0 10.I' 169 0.6 0.8 144 0.5 0.7 147 7.2 12.6 174 0.6 0.9 154 0.5 0.9 164 4.2 6.8 161 0.5 0.8 157 0.3 0.7- 194 2.3 5.7 246 0.2 0.6 245 0.3 0.6 217 3.9 8.1 206 0.4 0.7 198 0.2 0.5 290 4.0 8.8 221 0.3 0.7 201

Productivity Anomaly Index ·fPAI) was taken as the percentage of the technological trend productivity to the actual productivity. The PAI for the i'hyear is

(Pi -TPi) * 100 PAJ; =

TP; .... , ........... . .. .. (!)

Where PAl1 is the rice/wheat productivity anomaly index

for the ith year, P, is the actual rice/wheat productivity for the i'h year and TP1 is the technqlogical trend rice/wheat productivity for the ith year. '

RESULTS AND DISCUSSION

Norma_/ nakshatra based rainfall patt~rn and its variability

Mean nakshatra periods rainfall and rainy days and its standard deviation (SD) and coefficient of variation (CV) for the selected stations are·given in Table· I . Maximum rainfall and highest rainy days occ1.1rred during punarvasu nakshatra (July 6 - July 19) over both sites fo llowed by pushya (July 20-August 2). Even though mean_rainfall shows that rainfall received in all the nakshatras, as far as agricultural operations/ management is concerned, fairly gooci arn,ount ofrainfall have been received froin mrigashira (June 8-2 1) to hasta (September 27 - October 9) at Patna and rohini (May 25- June 7) to hasta

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33 SUBASH eta! . · [Vol. 13, No. I

Table 2: MK test results of rainfa ll and ra iny days at Patna and Sama:stipur districts (Z- MK statistic.s and Q-Sen 's Slope)

SL Nakshatra Rainfall ., '• ,, Raiqydays ·' . .. ~'No .. ~i ~-'" Pama Samastipur - - ~ - -~ Samastipur . . .

z Q z Ashwini -0.28 0 .000 0.49

2 Bharani 0.41 0.000 0.64 3 Kritika • 0 .75 0.000 0.93 4 Rohini 1.04 0.078 1.2 I 5 Mrigashira -0 .13 0.000 0.43 6 Aridhra 1.50 1. 179 1.87 7 Punarvasu 0.64 0.8 17 l.03 8 Push ya -0.89 -0.817 -0.28 9 As Iesha I. I I 0.195 0.34 10 Makha 0.74 0.529 - 1.0 I 11 Purbha 1.77 0.965 -0.22 12 Uttara -0.39 -0.263 -0.64 13 Has ta - 1.83 -0.857 - 1.86 14 Chitta -0.72 0 .000 Q.72 15 Swathi -0.38 -0.146 -0.55 16 Vi sh aka 0 .23 0.043 -0.03 +7 Anuradha -0.54 -0.026 -0.03 18 Jyeshta -0. 19 -0.0 10 -0.63 19 Moo la -0.89 --0.0 11 · -0.15_. 20 Purvashada -0.10 -0.025 -0.62 2 1 Uttarashada -0.4 1 -0.030 -0.46 22 Sravana -0~87 -0.05 1 -0.24 23 Dhanishta 0.07 r.-0.0 13 0.43 24 Shatabhista 0.25 .. ''0.057 . 0.84 25 Poorva Bhadra - 1.60 -0.067 -0.72 26 Uttara Bhadra - L IO -0.103 -0.55 27 Revati 0.34 0.037 0.77

at Samastipur. Moreover these periods received ~ 2 ra:i.ny days for both sites. The CV of rainfall shows below 100 % from aridhra (June 22-Jul)( 5) to uttara (September l3-26) at . Patna and from rohini to purbha (August 31 - September 12) at Samast ipur. This indicated that 7 and 8 nakshatra period's· received somewhat dependab le ra infall over Patna and Samastipur, respectively. Punarvasu nakshatra receives lowest CV of62 and 63 per cent, respectively for Patna and Samast ipur d istricts. ·

Tren ds of nakslwtr~ b;sed rainfall p attern

Time series of nakshatra rainfall and their trends at different stations are shown in Figs. I and 2 . The results of the MK tests on rainfall and rainy days are given in ·Table 2. As far as rice-wheat system is concerned, based on normal crop calendar and growing period, kriti ka to swathi ( 13 nakshatras) a re considered for r ice and vishaka to. uttara

. bhadra ( I J nakshatra~) are cons idered for wheat. Out o f 13 nakshatras during rice, ra infall shows ·decreasing trend in 6 nakshatras (rnrigashira, pushya, uttara, hasta , ch itta & swathi)

Patna

Q z Q z Q 0 .000 -0.43 -0.0073 0.94 0.0097

• 0.000 0.46 0.0049 . l.l 7 0.0151 0. 100 0.90 0 .0089 1.77 0.0276 0.321 1.63 0.0216 1.29 0.0271 0.203 -0.64 -0.0027 -0.36 -0.0040 1.536 2 .27 0.0400 1.93 0.0467 1.029 -0.60 -0.0185 0.42 0.0078

-0.348 -1.05 -0.0266 0.03 0.0064 0.264 0.93 0 .0211 -0. 13 -0 .0020 -0.856 -1.15 -0.0266 -0. 18 -0.0063 -0.054 0 .79 0.0257 0.08 0.0006 -0.389 -0.62 -0.0 148 -0.97 -0.0247 -0 .774 -1.61 -0.03 10 - 1.57 -0.0313 0.000 -0.75 -0.0032 0.87 0.0096 -0.022 0 .37 0.0090 0.08 0.00 10 -0.012 0 . 18 0 .0011 0.30 0.0003 -0.022 -0.34 . -0.0015 0.00 0.0000 -0.025 -0.16 -0.0002 -0.38 ~0.0020

0.000 -0.78 -0.0052 -0,27 -0.0030 -0.060 -0.03 -0.0028 0.25 -0.0016 -0.022 -0. 17 -0 .001 9 0.22 -0.0032 -0.0 16 -0.64 -0.0048 0.10 -0.0015 0.021 -0. 13 -0 .00 I 0 0.87 0 .0083 0.072 0 .53 0.0023 0.97 0.01 12 -0.098 -1.51 -0.iJ069 0.61 0 .0080 -0.1 37 -1 .6 1 -0 .0130 '-0 .28 -0.0071 0.167 0.34 0.0048 1.09 0.0.104

at ·Patna and 6 nakshatras (pushya, makha; purbha, uttara, hasta & swathi) at.Samastipur. A significant increasing trend of 0.9 mm y-1 and decre,asing trend of -0. 0.9 mm y·' , respectively have been notic~d. during_purbha and hasta at

· Patna.' However, a sign'ificant· in2teasing trend of 1.5 r:n m/ . year and decreasing trend of-:P-8 mm y·1

, respe~tively have been noticed during a rldhra and· hasta at Samastipur . . This significant decreasing trend of rai nfal I d uring hasta nakshatra may affect the su.Q_~equent wheat crop, particula rly during germination. As far as during wheat growing period is concerned majori-ty ofnakshatra periods showed·decreas ing trend of rainfall .for both sites. But none. of the trends are stat ist ically significant. During aridhra nakshatra a significant increasing trend in number of rainy days at the rate of 43 .3 and 46.3 per cent, respectively have been noticed over Patna and Samastipur. The increasing trend of ra infall d urin·g aridhra and decreas irig trend. of ra infa ll during pushya· nakshatra may provide an indication of shifting/mov ing of ra infa ll pattern early ie., shift ing of maximum ra infa ll period

. from pushya to punarvasu/aridhra.

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F ig 1: Rainfall variability and trends according to nakshatras at Patna district d uring I 960-2009

34

Verification of n akshatra based rainfall and rice-wlz et1t productivity varia_bility

Relation between aridhra rainfall and rice productivity: Out of30 negative rainfall anomaly years, 57 % of the years ( 17 years) r ice ·productiv ity a lso decreased be low technological trend at Patna. Interestingly, out of25 defic it rainfall years with ~ 20 % rainfall anomaly, 11 years rice

aridhra is very small (Table 3). As far as Samastipur is concerned, out of9 negative rainfall anomaly years during aridhra, 6 years (67 %) rice product iv ity . fa ll bel o w technological trend. This shows the percentage contribution of a ridhra rainfall on rice productivity vary with the location also.

• product ivity anomaly fall below IO % of the technolog ical trend. However, out of 19 deficit rainfall years with ~ 50 % rainfall anomaly, 7 years rice productivity anomaly fall below l 0 % of the techno logical trend. The correla tion coefficient between rice yield anomaly and·ra infall anomaly index during

Relation between swat/Ji rainfa!l and rice p roductivity: The mean rainfall of 4.8 and 7.2mm, respectively have been

· occurred during swathi nakshatra over Patna and Samastipur. At Patna, eleven years received rainfall ~ 2.Smm and only 3 years (I 974, 198 1 & 1997), the rice product_ivi ty fa ll b~ Iow technological trend product ivity (Table 4). Sim ilarly, a t Sarnastipur 5 years received rainfall e" 2.5rnm and only 2

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1970 1980 1990 2000 2010 Year

Fig 2: Rainfall variabi lity and trends according to nakshatras at Samastipur d i.str ict during 1960-2009

years ( 1989 & 2002), the rice productivity fa ll be lo~ technological trend productivity: It is a lso seen that rice productivity increased drastically from technological trend during the highest rainfall of7 l. 7 mm (about 29%) in 2003 at Patna. Similarly, at Samastipur alsc> recorded h ighest (50.5%) positive rice productivity in 2003 with a hig hest rainfall of 56.5 mm. All these years, except 1978 and 2003, received only one rainy day during swathi nakshatra over. Patna. In year 1997 and 2003, the rainfall distributed in 3 and 4 days respectively. Mo reover, in year 1997, the rainfall received in· two spe lls during the 14 day period, but 2003 it occurred in a s ing le spe ll. T his may be the reason fo r the negative productivi ty anoma ly in 1997 . Due to the occurrence of two

differe1. t rain spells: the length of moist period may have increased and th'.!reby spoiled the grains. But in Sarnast ipur in teresting ly more rainy days ( :?: 2) during swath! nakshatra helped to ach ieve higher rice producti vity. Correlation statistics a lso show that . higher F-val ue and significant relation between h ig her ri ce productivity a nd swath i nakshatra rainfall for'Samastipur district (Tabli' .3).

Relation between rohini, mrigaslzira, aridltra nakshatra rainfall and rice p roductivity: Rains occurred in six years during rohini and break during rnrigashira (8 June - 2 1 June) nakshatra and low rainfall during aridhra at Patna (Table 5). Out of these 6 years ( 1969, 1977, 1980, 1982, 1989 & 1992), 3

· - ··-.::.i..;~ __ ,...,_,__; ..+i~M·~

June201 l] Climate change adaptation strategies for agro-ecosystem 36

~!~bl,~ 3: F-Statistic and the .$year.man Rank ·correlation ~· ' ·T~ble 4: Rice productivity ~qgmaly index artd rainfall anomal~,;'' . . • ~ Coefficient (r.) betw.ee·n· ;~infall anomaly index -.. --<~ index during swathi naR\hatra hi deficit years at -.

during nakshatra ·and rice/.wheat productivity ·.. Patna and Samastipur distr~cts

anomaly index at Patna ansi Samastipur districts Year Rice productivity Rainfall

SI. Rainfall Patna Samas ti Eur No. anomaly F-Stat

indices r, F-Stat r,

!Rice I Aridhra 0.580 0.11 0.776 0.21 2 Swathi 1.354 . 0.12 14.470** 0.68++ 3 Rohini 1.817 0.19 2.335 -0.35+ 4 Mrigashira 0.069 -0.04 0.229 -0.12 Wheat 5 Hasta 0.011 0.02 0.064 -0.06

(* - 5 per cent level. **- I per cent level + - Significant at 0.05 level,++- Significant at 0.0 I level)

years the rice productivity falls below technological trend value. This means 50 % of the years this proverb holds well. Moreover, it is seen that in 1969 & 1992, the rice productivity declined drastically . . As far as Samastipur is. concerned, this situation occurred in only in 1992 and rice productivity reduced considerably to -60.9 % of the technological trend productivity. ·.

C"

Relation between hasta naksltatra rainfall anti wheat productivity: Out of.25 years received deficit rainfall during hasta nakshatra, 14 years i.e., 56% of the years the wheat productivity recorded negafo1e anomaly at Patna. However at Samastipur, out of 13 years received deficit rainfall during hasta, 8 years i.e. 62 % of the years the wheat productivity recorded negative anomaly. Rainfall failed .completely during hasta period in 4 years, out "f these, two years wheat productivity fall below the technological trend at Patna while at Samastipur, out of 3 years, 2 years wheat yie ld recorded negatively. Moreover, the good rainfall during hasta nakshatra may be beneficial for proper uniform I higher germination as well as larger coverage of cultivation of wheat and also increase the ground water table, so that farmers can afford recommended number of irrigations. Thus this traditional knowledge explains more than 50 % of yield anomaly in rice and wheat, which is more than the scientific findings ofrainfall and yield ofrice (Subash et al. 2009) and therefore, combining scientific and traditional knowledge may be more appropriate for forecasting yield in advance.

ACKNOWLEDGEMENT

Authors are thankful to N ational Ag ri cultural Innovation Project (NAIP) for providing necessary funds to taken up this study. The authors are also thankful to Joint Director, Agricultural Research Institute, Patna, Rajendra

Patna 1963 1972 1974 1978 1981 1989 1993 1994 1997 1999 2003 Samastipur 1989 1993 1997 2002 2003

anomaly index anomaly (%) index(%)

13.21 18.18 -4.26 27.18 -6.05 21.5

22.82 2.56

-17.78 4.41

29.4 1

-1.44 54.13 15.61 -9.04 50.50

-29.2(1) 120.8(1) 100.0(l) 358.3(1) 614.6(1) 254.2(1) 225.0( I ) -41.7(1) 725.0(3) 20.8(1)

1393.8(4)

169.4(1) 527.8(2) 73.6(2) -65.3(1) 684.7(3)

()parenthesis indicates number of rainy days

Agricultural University ar:i_g Head, IARI Regional Station, Pusa for sharing the meteorological data. ·

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37 SUBASH eta! [Vol. 13, No. 1

Table 5: Relation between rohini, mrigashira, a;idhra nakshatra rai~fall anomaly index and rice productivity anomal; index

~":! - - ~ Year Rainfall anomft~X index.(%) durin~ J< ,.Rice ' ,, .

~~

Rohini "Mrigash_ira Aridhra ~produdvity "=~- -. ~ •

Patna 1969 112.1 -53.5 1977 3.4 -83.5 1980 165.7 -11.1 1982 193.6 -17.5 1989 222.6 -12.4 1992 32.8 -78.5 SamastiEur 1992 4.9 -90.3

Mann, H.B. ( 1945) Non-parametric test against trend. Econometrica, 13:245-259.

Parthasarthy, B, Rupakumar, K and Munot, A.A. (1993). Homogenous Indian Monsoon Rainfall: Variability and ' prediction. Indian Acad Sci, Earth Planetary Sciences, 102:121 - 155.

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Monthly and seasonal rainfall series for all-India homogeneous regions and meteorological subdivisions: 1871-1994. Research Report No. RR-065, IITM, Pune, 113 pp . .

Sen, P.K. (1968) Estimates of the regression coefficient based on Kendall's taq. J. Am. Stat. Assoc., 63: 1379-1389

anomaly index (%)

-58.2 -32.99 -6.8 15.61

-60.8 5.67 -81.5 -1.20 -75.4 21.50 -32.9 -48.77

-79.4 -60.90

Subash, N. and Ram Mohan, H.S. (2010). Trend detection in rainfall and evaluation of standardized precipitation index as a drought assessment index for rice-wheat productivity over IGR in India. Int. J. Climatol., DOI: 10.1 OCJ21oc2188.

Subash, N., Ram Mohan, H.S. and Sikka, A.K. (2009). Quantitative assessment of influence of monsoon !;_ .

rainfall variability on rice production over India. · J :i.·

Agromet., 11(2):109-116. ,?i .

Vaidya, V.B. (2004). Long range forecast of monsoon with ~.·I.: Astronomx._and local observations for Maharashtra. A short communication published in AsianAgri-History, ,, 8(4):323-324 ;,

Varshneya,' M.C., Bhat, V.V. and Joshi, R.M. (2002). f "Nakshatra-varsha almanac prepared for Maharashtra" J pp.22. !

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Received: December 20 I 0; Accepted: April 2011 I I I

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