Remote Sensing for Crop Growth and Crop Simulation Modeling

Crop yield is the result of complex interaction among factors of soil, atmosphere, plant genotype, and management practices adopted. These complex interactions of crop with various factors and of factors among themselves make the crop yield modeling a difficult task. Several methods of crop yield forecasting have been developed ranging from purely statistical to agro-meteorological to empirical biophysical to mechanistic simulation models varying in complexities and input data requirements. Based on the methodology, all these yield models can be divided into two categories: (a)Empirical/Regression and (b) Simulation. The regression models assume that the variability in crop yield can be explained only by a few independent variables considered which are independent of each other. These regression models have many shortcomings due to errors in these assumptions. In contrast, simulation models closely represent a real crop by gradually growing leaves, stems, roots etc in response to external influences. In these models various mechanisms of photosynthesis, respiration, dry matter production and crop phenology are dynamically linked to each other by various rate equations and each mechanism is defined by the value of its state variables at the end of simulation time scale of one day. Some more complex simulation models include the subroutines of soil water and nitrogen balance to account for the effects of water and nitrogen stress on dry matter production. These models have good yield predictive capacity but are input data intensive. Dynamic growth simulation models are reported to have universal applicability compared with statistical models which are largely site-specific (Jamieson et al., 1991).