The science of agriculture depends on research activities for (i) the acquisition of knowledge; (ii) the ordering of knowledge and the development of understanding on that knowledge, and (iii) the application of the knowledge and/or understanding to the solution of practical problems (Rimmington and Charles-Edwards 1987). The mathematical models can be used in different ways within each one of these three activities. Basically a simplified description of a system, a mathematical model can help us to better understand the operation of a real system and the interactions of its main components. Thus, they are excellent forecast mechanisms. The important uses of mathematical models in agricultural sciences can be (i) analysis of observed responses in plant growth as a function of certain factors, to increase our understanding of the crop growth and to provide direction in our research; (ii) simulation of plant growth by models consisting of many interacting components and levels, as an aid for teaching and learning; and (iii) forecast of the plants response to certain climatic or management condition, as a tool for management and decision-making.
Besides its scientific importance, the simulation of plant yield has practical application in the management of cropping systems, in the formation of stocks, in the commercialization, in the making of agricultural policies and zoning, and in many other branches of agricultural activity. Before the model is applied for resource management, its accuracy needs to be tested within a given range of variables. Only then, it is wise to use the model to simulate the effects of different management techniques or environmental variations on the crop performance. The model should necessarily be used within its tested boundaries (Rimmington and Charles-Edwards 1987).
Was this article helpful?