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Articles

GROWTH PROCESSES AND CROP GROWTH MODELLING

Article number
174_23
Pages
193 – 204
Language
Abstract
During the past few centuries, mankind’s knowledge in all spheres has increased explosively.
Science as well as economy have responded to this development and are still responding by way of specialization (fig. 1). In agricultural science this process started about 1800 and caused a tremendous increase in yields.

Specialization, however, does not only lead to proggress; but it simultaneously creates problems.
One problem is, that single disciplines lose the general view of nature and economy, a fact which may cause disturbances, for instance by uncoordinated use of chemicals.
Furthermore, at an advanced level of production, the balancing and the coordination of all the production procedures becomes increasingly important, so that, in order to counteract the negative consequences of specialization, effective methods of integration are needed (s. fig.1).

This demand is not a new one.
Corresponding efforts can be traced back many decades, but success was limited.
A new chance presents itself by the development of the "general theory of systems" and of electronic data processing, which allow for the modelling of complex systems.
Conseqently, the number of publications has increased exponentially (fig.2).

Whereas the benefit of modelling is beyond question, the development and applicability of various lines of approach is under discussion.
In literature many classifications are being used (table). The models shown on the left hand side root in the natural sciences, such as physics and botany.
These models are considered to be based on the underlying physical and biological knowledge which has to be structurized and quantified.

The models shown on the right hand side, on the contrary, are explained to describe the behaviour of the system, such as a plant, disregarding the underlying, causal, e.g. biological processes.
This view may generally be accepted.
But it should be mentioned, that the methods of approach shown on the left hand side are not exclusively based on underlying real basic knowledge, but are additionally making use of assumptions and adaptation parameters.

Publication
Authors
H. Krug
Keywords
Full text
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