192 
model is being used to determine the 
initial value of GLAI. In the later 
case, a fixed functional relationship 
must be assumed for GLAI. 
Because the model process is quite 
sensitive to an initial value, an 
accurate computation of an initial 
value is important in determining a 
final yield potential. Although one 
satellite acquisition is sufficient to 
establish an initial value, more 
acquisitions can provide a better fit. 
The model determines the following on a 
daily basis: (1) absorbed light based 
on leaf area index (LAI); (2) above 
ground drymass; (3) the increase or 
decrease in LAI based on drymass 
production; and (4) growth stage on a 
daily time step basis. Growth stage is 
determined by accumulating degree 
days. Yield is determined by 
accumulating a fraction of daily 
drymass production between anthesis 
(flowering) and maturity. 
A prototype operational model ,^jca1 led 
GRAMI, has been developed for use where 
producer information can be obtained, 
such as in the United States. The 
model requires air temperature and 
solar irradiance on a daily basis and 
an estimate or knowledge of the 
planting date of the field. Rainfall, 
evaportransportation, and other 
difficult to obtain or highly spatially 
variable data are not required since it 
is assumed that observations of leaf 
ctnopy development implicitly 
incorporate these measurements. 
Satellite observations can be used to 
determine daily solar irradiance and 
temperatures may be determined from 
either ground observation or 
potentially from satellite soundings. 
For yield forecasting, the model would 
be run up to the current day using the 
appropriate inputs, then, a regression 
model could be used to project yield to 
harvest. A more appealing method would 
be to use, instead of a regression 
technique, model simulations of 
"future" weather or scenarios based 
upon climatic information. Again, the 
model assumes that water stress is 
implicitly accounted for in the 
remotely sensed observations of canopy 
development. 
THE FUTURE--PROSPECTS LOOK MODERALLY 
BRIGHT 
The CROPCAST system Is deemed as 
commercially viable by its parent 
organization. GOSSYM-COMAX is becoming 
a more widely used tool for management 
decisions and recent research is 
indicat in ;■» that more simply structured 
mechanistic models may be suitable for 
large area application. Clearly, there 
is a need for crop yield forecasts and 
indeed, given the condition of the 
world today and the movement of 
governments toward market economies, 
there should be an increasing demand 
for more extensive and accurate 
forecasting methods. 
REFERENCES 
a) Acock, B. 1989. Crop Modeling in 
the USA. Technical Communications of 
the International Society of 
Horticultural Sciences, Number 248. 
b) Baker, D. H., Lambert, J. R., and 
McKinion, J. M., 1983. GOSSYM: A 
Simulator of Cotton Crop Growth and 
Yield. South Carolina Agricultural 
Experiment Station, Bulletin 1089. 
c) Birkett, T. R., 1990. The New 
Objective Yield Models for Corn and 
Soybeans. Estimates Division, 
National Agricultural Statistics 
Service, U.. Department of 
Agriculture, Staff Report No. 
SMB-90-02. 
d) Boatwright, G. 0., Badhwar, G. D. 
and Johnson, W. R., 1988. An AVHRR 
Spectral Based Yield Model for Corn 
and Soybeans. Contract Report, 
Project No. 8103, ITD, Space Remote 
Sensing center, NSTL Station, MS. 
e) Cotter, J. and Nealon, J., 1987. 
Area Frame Design for Agricultural 
Surveys. Research and Applications 
Division, National Agricultural 
Statistics Service, U.S. Department of 
Agriculture. 
Lemmon, H., 1986. COMAX - An Expert 
System for Cotton Crop Management. 
Science 233:29-33. 
Maas, S. J., 1988a. Using Satellite 
Data to Improve Model Estimates cf 
Crop Yield. Agronomy Journal 
80:655-662. 
Maas, S. J., 1988b. Use of 
Remotely-s ensed Information in 
Agricultural Crop Growth Models. 
Ecological Modelling, 41:247-268. 
f) Matthews, R. V., 1985. An Overview 
of 1985 Corn, Cotton, Soybean and 
Wheat Objective Yield Surveys. 
Statistical Research Division, 
Statistical Reporting Service, U.S. 
Department of Agriculture. 
g) U.S. Department of Agriculture, 
Statistical Reporting Service, 1983. 
Scope and Methods of the Statistical 
Reporting Service. Mise. Publ. No. 
1308 . 
Wiegand, C. L. and Richardson, A. J., 
1990. Use of Spectral Vegetation 
Indicies to Infer Leaf Area, 
Evapotranspiration and Yield: I. 
Rationale. Agronomy Journal 
82 : 623 -629 .