Maclean et al. - 2002 - Rice almanac source book for the most important e

2. Water-limited production.
3. Water- and N-limited production.
4. Water-, N-, and other nutrient-limited
production.
Going from type one to type four, production
generally decreases and the variables that
determine system behavior increase. At all
levels, growth-reducing factors such as insects,
pathogens, and weeds can be introduced. Models
for all production levels can be developed. Models
at the first level are further developed than
models at the others.
Well-developed models that simulate the
growth of a crop in relation to its dynamic
environment can be used to help prioritize
research. Crop modeling combined with
geographic information systems (GIS) analysis
enables researchers to distinguish agroecological
zones and to rank quantitatively the technical
constraints to agricultural production within
them. These models allow the impact of new
technology on agricultural production to be
assessed before the technology is introduced.
The GIS database can link the models directly
with socioeconomic aspects.
Crop simulation models have many uses.
Models can be used as a research tool and to
support problem solving, risk assessment, and
decision making. They can guide researchers in
prioritizing their research and in integrating
quantitative knowledge from different disciplines.
Also, models can be used as a framework
for training. Further, models can be used to
extrapolate research findings over broad regions
and extended time, since the models account for
crop-environment interactions. Using long-term
weather data, yield probabilities can be
simulated.
Crop models are particularly useful in the
rainfed lowland rice ecosystem, which is
characterized by high temporal variability and
spatial heterogeneity of the environment. A
limited number of field experiments cannot
provide a reliable basis for management
strategies under the myriad conditions that exist.
Simulation models can replace expensive and
time-consuming experiments because of their
ability to generalize experimental findings and
help interpret the results of a few selected
experiments.
An aspect that is beginning to gain more
importance is the use of models to set breeding
goals. The physiological attributes that
contribute significantly to crop production in a
given environment lend themselves to definition
by crop modeling. Models can serve to bridge
between functional genomics, ecophysiology,
and agronomy. Ecophysiological models are used
to analyze the influence of different plant
parameters (e.g., plant architecture, nutrient
status, partitioning) on desired agronomic traits
such as yield, weed competitiveness, or drought
tolerance. Functional genomics supplies genetic
markers for these traits, which then assist
breeders during the selection process.
Modeling can also improve crop
management. In West Africa, for example, a
framework was developed combining the use of
simulation models, field data, and long-term
weather data to design site-specific crop
management options. This helps farmers to
improve the timing of seeding, transplanting,
irrigation, and fertilizer application, as well as to
determine type and dose of fertilizer in a range of
biophysical and socioeconomic environments.
One component, a decision tool called RIDEV, is
already widely used by extension agencies in
Senegal and Mauritania.
Modeling is especially useful in yield gap
analysis, a method for identifying constraints to
agricultural production in different agroclimatic
zones. From yield gap analysis, constraints that
can be reduced can be identified. Researchers
then concentrate on ameliorating those factors
that contribute to the gap between farm yield,
potential farm yield, and potential experiment
station yield (Fig. 4).
Potential
station yield
Potential
farm yield
Yield gap 1
Yield
gap 2
Actual
farm yield
Weather
Variety
Water
Nutrients
Weeds
Insects
Diseases
Fig. 4. Modeling applied to yield gap analysis helps
to identify constraints that can be reduced, thus
contributing to higher yields in farmers' fields.
44 Rice almanac