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Use of satellite data and farmers eye estimate for crop yield modeling 2.1 Study area and extent of data used in the study The study was conducted for district Rohtak of Haryana State which is one of the major wheat growing areas having an acreage of more than 66 percent under wheat crop during Rabi season. Following data were used in the study (a) General crop yield estimation survey data The yield data for the Rabi season for the years 1995-96 and 1997-98 from general crop estimation surveys based on crop cutting experiments for wheat crop for district Rohtak, Haryana has been used. (b) Satellite data The satellite data in the study has been used for 1995-96 from IRS-1B, LISS-II of path 30 and Row 47 of Feb.17, 1996. The total area of Rohtak district is covered in one sub scene B2 of 30-47. For 1997-98 IRS-1D data of sensor LISS-III of path 95 and row 51 for Feb.4, 1998 has been used. A Global Positioning System (GPS) was also used to identify the exact locations of the plots selected for crop cutting experiment for wheat crop in terms of their latitudes/longitudes and also the locations of ground control points(GCP's) which were later used to rectify the raw digital spectral data. A topographic map is the best tool to supply ground truth information for visual interpretation and identification of various features on satellite imageries. From these maps locations of villages along with related features like continuous roads, canals railway tracks etc. can be easily identified on FCC’s. Survey of India topographical maps of Rohtak district on 1:50,000 scale were used to identify the location of villages selected for the crop yield estimation surveys. (c ) Farmers yield appraisal data The farmers eye estimate data has been collected for the years 1995-96 and 1997-98 for wheat crop yield from the same farmers whose fields have been selected for crop cutting experiments in general crop estimation surveys. The data should be collected for eye estimate of yield for only the same fields at the time of maximum crop growth stage where satellite data has highest correlation with yield. 3. Integrated yield model using spectral data and farmers eye estimate of crop yield Most of the crop yield models developed so far could not be adopted in practice either because of delay in the availability of data on different variables to be used in the model or the high cost in collecting the data and in analysing the results. For any operational yield model to be successful for adoption it is necessary that data should be available much before the harvest of the crop and it should be cost effective. Spectral data in the form of vegetation indices have proved to be very useful variable for explaining variability of the crop yield which can be early available for use in yield forecasting models. In a recent study for ‘evaluation of crop cutting methods and farmers reports for estimating crop production’ undertaken at Longacre Agricultural Development Centre UK, it has been shown that farmers eye estimates are remarkably close to actual production figures. But, eye estimates being subjective and amenable to several non-sampling errors, it is desirable that these estimates are not used directly for estimation of crop yield. However, this information can be used as auxiliary variable along with the spectral vegetation indices to improve the efficiency of the crop yield models. An earlier such attempt on using eye appraisal of crop yield of a large number of sample fields as auxiliary information had been made by Panse, Rajgopalan and Pillai (1966). In the present study, therefore suitable models using spectral vegetation indices in the form of NDVI and farmers eye estimate as explanatory variables in the regression model have been developed for improved crop yield forecasting models. For developing the models, wheat crop yield data for Rabi 1995-96 from GCES and also the farmers eye estimate of crop yield for the corresponding plots for district Rohtak and the corresponding satellite spectral data for Feb. 17, 1996 from IRS IB- LISS II in the form of vegetation indices NDVI and RVI have been used. For testing the model the respective data has been taken for 1997-98. The results show that the predicted yield is very close to the actual yield in almost all the models. However the most efficient model is achieved when the satellite data in the form of NDVI along with the farmers eye estimate of crop yield are used as independent variables. In this case the value of R2 is 0.90 with a standard error of 1.02 and the predicted value is very close the actual value with a standarderror of approximately 5%. 
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