Numerous dynamic crop models have been developed for simulating crop growth in function of environmental factors (soil characteristics, climate) and of agricultural practices. Some of these models can be used for predicting crop biomass and yields and crop quality before harvest. For example the Geographic Information System (GIS) was successfully used to predict water-limited biomass production potential of various agro climatic zones of the world (Fig 3). It is very clear that the biomass producing potential of

SAT is between 300 to 600 g dry matter M-2 Y-1 that corresponds well with the observed annual productivities.

The advent of remote sensing technology supported by Geographic Information System (GIS) has opened new vistas of improving agricultural statistics systems all over the world. The applications of Remote Sensing (RS) in the field of agriculture are wide and varied, ranging from crop discrimination, inventory, assessment and parameter retrieval, on one hand, to assessing long term changes and short-term characterization of the crop environment. The use of remote sensing for crop acreage and yield estimation has been well demonstrated through various studies all over the world, and has gained importance in recent years as a means of achieving these estimates possibly in a faster mode and at a cheaper cost (Murthy et al., 1996). An integrated methodology for providing area and yield estimation and yield forecasting models with small area estimates at the block level using satellite data has been developed (Singh and Goyal, 2000; Singh et al. 2002).

The remote sensing use for drought prediction can benefit from climate variability predictions. Recent research on crop-water relations has increasingly been directed towards the application of locally acquired knowledge to answering the questions raised on larger scales. However, the application of the local results to larger scales is often questionable. Crop simulation models, when run with input data from a specific field/ site, produce a point output. The scope of applicability of these simulation models can be extended to a broader scale by providing spatially varying inputs (soil, weather, crop management) and combining their capabilities with a Geographic Information System (GIS). The main purpose of interfacing models and GIS is to carry out spatial and temporal analysis simultaneously as region-scale crop behavior has a spatial dimension and simulation models produce a temporal output. The GIS can help in spatially visualizing the results as well as their interpretation by spatial analysis of model results.