the history of science. Its domain of reference 
has steadily expanded beyond the realm of cognitive 
science and intelligence, and into the multiple 
assimilations of neurobiological structure-function 
relationships. Any major cognitive process can be 
analyzed into component subprocesses. Analysis of 
subprocesses will then suggest principles that can 
also be used in models of other processes. The in 
puts to such analyses would come through the fea 
ture, or input, nodes that upon activation would 
excite the response patterns of category, or out 
put, nodes. In between are the internode or hidden 
connections that determine the strength of the 
final outcome. For a given application, the 
adaptive-resonance, back-propagation, or brain- 
state- in-a-box theories might be suitable in some 
ways but not in others. The formulation stage es 
sentially decides the algor ithm selections for 
application specifics. 
A network is established with a pre-set bias to 
wards the features considered important for the 
problem. The network's recognition of the problem 
features is sustained and recursively enhanced by 
the strength of the weighted connections. A "train 
ed" network when presented with subsequent, (and 
often incomplete) problem definition will recourse 
to the most converging solution derived from its 
adaptive learning. As per implementation, the 
artificial neural networks use both hardware and 
software emulation to achieve the desired synthesis 
of information. 
Adapting to Land Evaluation 
Land capability assessment is an interweave of a 
host of variables, with positive and negative cau 
sative inheritance, that, in the evaluators judge 
ment, prevail on the utility of the land. Depend 
ing upon the region of interest, the sum total of 
variable inheritance could change significantly 
enough to require skewed decision making. The 
changes, and their subsequent impact on the recom 
mendations on utility of the land may be so unique 
as to disfavor harnessing conventional computing 
prowess. Under such circumstances, when step-wise 
integrations of causative factors is likely to 
yield the best results, the use of neural network 
simulations is not unwarranted. 
For neural network to simulate irrigation suita 
bility assessment, it would have to focus on those 
agronomic and management parameters that are class 
determining, i.e. their significance in affecting 
the evaluation of land is recognizable. Their in 
teraction is best understood under local farming 
conditions, and the attributes of land productivity 
may not be transferable elsewhere. The suitability 
assessments would, therefore, be highly responsive 
to the relative productivity thresholds of the loc 
al area. For a simulation to be considered comp 
lete, it must not only facilitate arrival at clas 
sified decision making but also expose the relative 
contribution of each significant input parameter to 
the overall categorization. If the network has 
been trained on consistent integrity data sets, the 
outcome would be devoid of invalid extractions. 
This consistency gives itself to tremendous useful 
ness when the operator is constrained by availabil 
ity of inputs or when suitability of inputs or when 
suitability trends are being assessed for large 
problem cases. 
METHODOLOGY 
Principal component separations on geometrically 
corrected multiband SPOT XS data yielded the tenta 
tive locations of sampling sites whore contrast of 
crop stands vs bare soil appeared optimum. October 
(1989) was selected for satellite coverage in order 
to expose maximum differentiation between the cotton 
crop, fallow land, and the village hutments/bare 
soil. Standard parallelpiped classified distin 
guished between these principal land use classes 
throughout the Froject area (fig. 2). The classi 
fication map (at 1:20,000 scale) facilitated land 
unit boundary delineations for match-up against the 
land utilization types. Reference to spatially 
sensitive locales, e.g. distributaries was estab 
lished with the superimposition of panchromatic 
coverage over the classified image. 
A questionnaire, invoking the principal agronomic 
and management considerations, was prepared to as 
sist towards farmer interviews. Disturbed soil sam 
ples were taken from pits along three horizons of 0- 
15 cm, 15-30 cm, and 30-40 cm to be evaluated later 
for topsoil stratification. The combines of ques 
tionnaire information and soil tests were processed 
through a PC-based back propagation neural network 
weighted to give thresholds along pre-set B/C ratios 
for equity considerations. The redeeming feature of 
the neural network (NemoShell from Wards Systems 
Group, USA) is the ability to afford subjective de 
cision-making against pre-set error criteria. Each 
suitability assessment was preempted by the econo 
mics of the land use management, over: land holdings 
of varying size. 
Amongst the specifications for land use requirements 
and limitations included in the questionnaire were 
such class determining factors like crop types grown 
at seasonal turn-arounds, growing periods, fallow 
land requirements, seeding rates, and yield/ha for 
different varietals. Soil nutrient capacity, in ad 
dition to saturation extract determinations on cat 
ionic and anionic concentrations, both available 
and exchangeable, were some of the principal deter 
minants of quality of the soil. Economic inputs, 
like use of pesticides, fertilizers, crop-specific 
watercourse charges, operational use of tube wells 
by owners, and rent charges for on-farm/locale- 
specific mechanization fm laud preparation, in 
addition to labor supply at peak demand periods were 
included in the assessments foi the evaluation of 
B/C ratios. 
V 
Network Implementation 
The class determining fact ms were arranged in the 
neural network so as to allow ranged selections ac 
ross physical and chemical characteristics of the 
soil and the quality of irrigation water such that 
they become input not only for the economic returns 
but also introduced a measure of suggestibility 
through land development costs in the use of amend 
ments (as is typical of saline/alkali and non-saline 
alkali soils). 
The network was configured to 100 agronomic and ma 
nagement characteristics that were to serve as the 
primer for ultimate reflections on equity considera 
tions. The original variable selections were divid 
ed into 4 subgroups, namely water quality, physical 
attributes ol the soil, and saturation extract (1:5) 
cationic and anionic concentrations. Twenty-five 
sample case summaries were prepared from amongst the 
combines of subgroups for irrigation suitability 
categor izations SI, S2, S3, N1. ami N2 (in the order 
of most suitable to permanently not suitable). The 
intermediate node settings were defaulted to tire 
number of input nodes for model simplification and 
to minimize on the computing time. The error cri 
teria for weighted convergence was set to 0.005.