+332.376x SPI1_9 — 458.27x SPI2_7 
- 204.95 x SPI3_10 + 459.08 x SPI3_8 
r?=0.,51 n=17 df=11 
Dharwad Yield = 1314.99 + 126.345 x SPI 1_9 
+ 68.03 x SPI3 8 
r2=0.36 n= 17 df=i4 
  
  
1400 
  
1000 4 
  
YIELD Kg/ha 
  
  
  
600 TT T T T T T T T T T T T T T T T f T T 
9 FR DPD HT PP SE 
S dC 
CESSE ESS 
IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India,2002 
  
—o— ESTIMATED YIELD —&— REPORTED YIELD —— PREDICTED YIE 
  
  
  
Fig. 3. Yield forecast of Sorghum in Gulberga district of 
Karnataka state using SPI index method. 
  
  
E 
  
YIELD Kg/ha 
[91] 
8 
  
  
  
  
g 
re re te TY IT TE LTT oT 
S o «€ d d o go S 
SPESEN TE 
—a— ESTIMATED YIELD —x— REPORTED YIELD —a— PREDICTED YIELD 
  
  
  
  
Fig. 4. Yield forecast of Sorghum in Dharwad district of 
Karnataka state using SPI index method. 
Where, SPI1 6,7,8,9 & 10 are monthly SPI of June, 
July, August, September & October respectively. 
Similarly, SPI2_6,7,8,9 & 10 are moving average of two 
month SPI of June, July, August, September & October. 
The precipitation at early and mid .season showed 
positive response to yield. The rainfall at June and July 
are generally scarce but it played a vital role in 
determining the sowing time and it increases the residual 
soil moisture for better germination. The relative 
deviation of the forecast to the reported estimate have 
been within 10 % for most of the years in all the 
districts. This shows, that SPI have been a better 
indicator of crop yield, especially in rainfed conditions. 
  
\/ 
i 
  
QN e 
m OT YEAR 
  
  
  
| ESTIMATED YIELD — —— REPORTED YIELD — —4— REDICTED YIELD 
  
   
Fig. 5. Yield forecast of Sorghum in Raichur district of 
Karnataka state using SPI index method. 
5. CONCLUSION 
SPI is shown to be another indices for predicting yield 
of rainfed crops like sorghum in Karnataka state. SPI 
has been found to be a new way of analyzing the rainfall 
data and assessing its impact on sorghum yield. This has 
shown effective tool for predicting sorghum yield in 
drought years. 
ACKNOWLEDGEMENTS 
This study would not have been possible without 
constant support and encouragement provided by Shri 
J.S. Parihar, Group Director, Agricultural resources 
group and Mission Director, RSAM. The team is also 
grateful to Dr. Reddy and Dr Vijay Kumar, Scientists in 
DMC, Bangalore for providing all necessary data and 
figures as and when required. 
REFERENCES 
Dutta S., N.K. Patel, S.K. Srivastava, R.B. Singh, L.R.P. 
Singh and B.K. Sinha (2001). Districtwise agro- 
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Dutta, S., Nain, A.S., Dadhwal, V.K., and Prakash, V.S. 
(2002). Development of taluka/block level sorghum 
yield model using crop water requirement index model, 
CERES-SORGHUM simulation model and standardised 
precipitation index (SPI) model in Karnataka state. 
Scientific note: RSAM/SAC/FASAL TD/SN/13/02. 
Guttman, N.H. (1999). Accepting the standardized 
precipitation index: a calculation Algorithm, J. of the 
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Lana X., Serra C., and Burgueno A. (2001). Patterns of 
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