Introduction

Crop is defined as an "Aggregation of individual plant species grown in a unit area for economic purpose".

Growth is defined as an "Irreversible increase in size and volume and is the consequence of differentiation and distribution occurring in the plant".

Simulation is defined as "Reproducing the essence of a system without reproducing the system itself". In simulation the essential characteristics of the system are reproduced in a model, which is then studied in an abbreviated time scale.

A model is a schematic representation of the conception of a system or an act of mimicry or a set of equations, which represents the behaviour of a system. Also, a model is "A representation of an object, system or idea in some form other than that of the entity itself". Its purpose is usually to aid in explaining, understanding or improving performance of a system. A model is, by definition

Satellite Remote Sensing and GIS Applications in Agricultural Meteorology pp. 235-261

"A simplified version of a part of reality, not a one to one copy". This simplification makes models useful because it offers a comprehensive description of a problem situation. However, the simplification is, at the same time, the greatest drawback of the process. It is a difficult task to produce a comprehensible, operational representation of a part of reality, which grasps the essential elements and mechanisms of that real world system and even more demanding, when the complex systems encountered in environmental management (Murthy, 2002).

The Earth's land resources are finite, whereas the number of people that the land must support continues to grow rapidly. This creates a major problem for agriculture. The production (productivity) must be increased to meet rapidly growing demands while natural resources must be protected. New agricultural research is needed to supply information to farmers, policy makers and other decision makers on how to accomplish sustainable agriculture over the wide variations in climate around the world. In this direction explanation and prediction of growth of managed and natural ecosystems in response to climate and soil-related factors are increasingly important as objectives of science. Quantitative prediction of complex systems, however, depends on integrating information through levels of organization, and the principal approach for that is through the construction of statistical and simulation models. Simulation of system's use and balance of carbon, beginning with the input of carbon from canopy assimilation forms the essential core of most simulations that deal with the growth of vegetation.

Systems are webs or cycles of interacting components. Change in one component of a system produces changes in other components because of the interactions. For example, a change in weather to warm and humid may lead to the more rapid development of a plant disease, a loss in yield of a crop, and consequent financial adversity for individual farmers and so for the people of a region. Most natural systems are complex. Many do not have boundaries. The bio-system is comprised of a complex interaction among the soil, the atmosphere, and the plants that live in it. A chance alteration of one element may yield both desirable and undesirable consequences. Minimizing the undesirable, while reaching the desired end result is the principle aim of the agrometerologist. In any engineering work related to agricultural meteorology the use of mathematical modeling is essential. Of the different modeling techniques, mathematical modeling enables one to predict the behaviour of design while keeping the expense at a minimum. Agricultural systems are basically modified ecosystems. Managing these systems is very difficult. These systems are influenced by the weather both in length and breadth. So, these have to be managed through systems models which are possible only through classical engineering expertise.

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