Geoscience Reference
In-Depth Information
information resources. The information gathering process is
cumbersome and sometimes unreliable. Often the task to select,
combine and analyse the information is demanding. As infor-
mation technology has opened up new challenges to automate
data and analysis, computer programmes that simulate the crop
growth or yield of crops under different management regimes,
help farmers make technical decision to manage their crops bet-
ter. Since 1960, the large-scale evolution of computers allowed
the ability to synthesise detailed knowledge on plant physi-
ological processes in order to explain the functioning of crops
as a whole. Insights into various processes were expressed using
mathematical equations and integrated in simulation models.
Therefore, a model can be defined in different ways by scientists:
(a) A model is a schematic representation of the conception of an
agricultural system or an act of mimicry or a set of equations,
which represents the behaviour of a system. (b) A model is 'a rep-
resentation 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 the performance of a system.
In the beginning, models were meant to increase the under-
standing of crop behaviour by explaining crop growth and
development, in terms of understanding physiological mecha-
nisms (Bachelet et al., 1993). Over the years, new insights and
different research questions motivated further development
of simulation models. In addition to their explanatory func-
tion, the applicability of well-tested models for extrapolation
and prediction was quickly recognised and more application-
oriented models were developed. For instance, demands for
advisory systems for farmers and scenario studies for policy
makers resulted in the evolution of models geared towards tac-
tical and strategic decision support, respectively. Now, crop
growth modelling and simulation have become accepted tools
for agricultural research (Boote and Toolenaar, 1994).
3.2
types of crop models
Depending on the purpose for which they are designed, models
are classified into different groups or types.
Statistical
models
These models express the relationship between yield or yield
components and weather parameters. In these models, relation-
ships are measured in a system using statistical techniques. In a
statistical model approach, one or several variables (represent-
ing weather or climate, soil characteristics or a time trend) are
