Geoscience Reference
In-Depth Information
Abstract
Crop modelling can play a significant part in systems
approaches by providing a powerful capability for sce-
nario analysis. Crop modelling has developed extensively
over the past 30 years and a diverse range of crops models
are now available. It is argued, however, that the tendency
to distinguish between and separate the so-called 'sci-
entific' and 'engineering' challenges and approaches in
crop modelling has constrained the maturation of model-
ling. It is considered that effective crop modelling must
combine a scientific approach to enhance understanding
with an application orientation to retain a focus on predic-
tion and problem solving. Greater use of crop simulation
models has also been suggested to increase the efficiency
of different trials. While simulation models successfully
capture the temporal variation, they use a lumped param-
eter approach that assumes spatial variability of the soils,
crops or climate.
3.1
Introduction: Crop weather simulation
modelling
Crop is defined as 'aggregation of individual plant species
grown in a unit area for economic purpose', whereas irrevers-
ible increase in size and volume and the consequences of dif-
ferentiation and distribution occurring in a plant is known as
growth
. Reproducing the essence of a system without repro-
ducing the system itself is called
simulation
. In simulation,
the essential characteristics of the system are reproduced in a
model, which is then studied in an abbreviated time scale.
The agricultural region can be considered a collection of
individual fields that vary in environmental conditions and man-
agement practices. An increase in the population, demands an
increase in agricultural production with available resources.
Efficient management of available resources with variable
weather conditions is essential to increase the productivity of
agriculture. In addition to this, the focus of agricultural produc-
tion is changing from quantity towards quality and sustainability
(Aggarwal et al., 1997). Solution of these new challenges requires
consideration of how numerous components interact to effect
plant growth. These transitions force farmers and agricultural
advisors to deal with increasing bulks of information (Aggarwal
et al., 2006). They need to analyse vast and sporadically located
