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Use of satellite data and farmers eye estimate for crop yield modeling
2 Use of Satellite data in crop yield modeling.
Forecasting of crop production is one of the most important aspect of agricultural statistics system. Yield forecasts at present are based on quite subjective estimates and the final crop production estimates based on objective crop cutting surveys become available long after the harvests. This as such calls for the necessity of objectives methods for pre-harvest forecast of crop yields.
The main factors affecting crop yield are inputs and weather. Use of these factors forms one class of models for forecasting crop yields. The other approach uses plant vigour measured through plant characters. It is assumed that plant characters are integrated affects of all the factors affecting yield. Yet another approach is measurement of crop vigour through remotely sensed data. These approaches are being tried by various organizations.
Box and Jenkins (1976) used time series models for forecasting where the variation in yield during different years is explained using historical data through trend analysis and presented the well-known technique of auto regressive integrated moving averages ARIMA.
The approach using weather parameters is normally based on time series data. The major work in this regard has been attempted at IMD (Sarker, 1977, Sarwade, 1988). Their studies involve identification of significant weather parameters in different periods and utilizing these parameters in the regression model along with trend. At IASRI, studies have been carried out at district level using weekly weather parameters. Various composite weather variables were derived as weighted accumulations of weekly weather parameters/interactions up to the time of forecast and were used as regressors in the model along with trend. Principal components of weather variables were also tried for developing the model (Agarwal et al.1986; Jain et al. 1980). The problem associated with meteorological model is assumption of same weather prevailing in a larger area as observatories are sparsely located. These models also require long series of data, which are not available for most of the locations.
In case of crop yield modeling using satellite data, several studies have been undertaken to establish relationship between spectral parameters through vegetation indices and the crop yield. Sridhar et. al.(1994) presented wheat production forecasting for a predominantly un-irrigated region in Madhya Pradesh. Singh and Ibrahim (1996) examined the use of multi date satellite spectral data for crop yield modeling using Markov Chain Model. Saha (1999) used satellite data and GIS for developing several crop yield models. Crop yield is influenced by a large number of factors related to soil, weather, agro climatic factors, management practices etc. Satellite data is integrated manifestation of effects of all these factors on the crop growth and hence can provide immense potential for use in crop yield modeling.
Several approaches in crop yield modeling using satellite remote sensing data have recently been developed like spectral yield models using spectral vegetation indices or spectral growth profile, meteorological yield models using meteorological data pertaining to some significant crop phenological stages in the form of some indices. Agriculture and climate are closely inter linked in the sense that crop growth development and production are greatly affected by variation in agro-meteorological parameters during crop growth period. In this modeling approach remote sensing derived SVI is coupled with meteorological indices and multiple regression model is developed. A large number of meteorological indices like Growing Degraded Day (GDD), Mean Temperature (Tmean), Rainfall index (RI), Crop Water Stress Index (CWSI) etc are being used in agromet spectral yield models. However the difficulty and delay in availability of weather parameters make this approach less attractive. Studies have also been conducted for developing integrated or combined models incorporating parameters from diverse sources or combining two or more independent forecasting models.
Recently with a view to collect, collate and assimilate large data from different sources, a National Crop Forecasting Centre (NCFC) has been set up under the Ministry of Agriculture during 1998. Deptt. of Space have also recently launched a project – Forecasting Agricultural output using Space, Agro-meteorology and Land based observations (FASAL) envisaging advance reliable assessment of crop acreage and production using remote sensing techniques and also other data. Very recently National Wheat production forecast for (1998-99) using multi date WiFS and meteorological data have been developed under this project
A study on "Evaluation of crop cut method and farmers reports for estimating crop production"(Verma et al (1988)) was undertaken at Long acre Agricultural Development Centre UK. This study was carried out in 5 countries in Africa during 1987 with the objective of comparing crop estimates based on crop cut methods with estimates obtained by asking farmers directly to state their production. The results of the study showed that farmers eye estimates are remarkably close to actual production figures in all the countries and they also show considerably small variance compared to the estimates based on crop cutting experiments. After the publication of this report considerable interest is again focused on using farmers estimates which are much cheaper to obtain and easier to conduct.
In the present study, therefore an effort is made to use the farmers eye estimate more objectively as a auxiliary variable along with the spectral indices to improve the efficiency of crop yield models for forecasting crop yield. Farmers estimates were obtained for the same fields in which crop cutting experiments were conducted.
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