Crop Growth Simulation Models

1. Introduction Systems approach has been used by engineers since the 1950's for the study of complex and dynamic processes but their use by scientists working in biological fields is relatively recent. A system is defined as any well delineated part of reality which may contain a number of interrelated elements forming the systems structure. These functioning components interact in various ways and the interaction between components can be physical, economic or social. A system is defined by the user on the basis of objectives and the intrinsic structure of the system as measured and observed. For an agronomist a system may be represented by a rice crop with leaf, stem and root (plant organs) and growth and transpiration (processes) as elements which interact strongly. A system is 'dynamic' when its state changes over time. This change could be 'continuous' when its behaviour and state change relatively slowly or 'discrete' when the changes are fast, sudden or large. A model is a simplified representation of a system and simulation is the study of the system and its behaviour using models. 2. Models Planners, economists and researchers have always been interested in finding out ways and means to estimate crop yields in advance to the extent possible. With this objective, several regression models have been developed by many workers to predict the relationship with agricultural productivity and its components. These models when dealing with multi-year time series usually include a technology trend factor, thus lumping everything other than climatic factors in one regressor. However, such analysis where the effects of some specific factors is determined without consideration of interactions and feedbacks from other controlling elements can often be misleading. In addition to the climatic factors, there is a large number of edaphic, hydrologic, biotic, agronomic and socioeconomic factors that influence crop growth and productivity. Crop growth is a very complex phenomenon and a product of a series of complicated interactions of soil, plant and weather. Dynamic crop growth simulation is a relatively recent technique that facilitates quantitative understanding of the effects of these factors, and agronomic management factors on crop growth and productivity. These models are quantitative description of the mechanisms and processes that result in growth of the crop. The processes could be crop physiological, meteorological, physical and chemical processes. Such a modelling assumes that the rate of change of system can be closely approximated by considering the rate of …