MSc

Agricultural scenario of Kerala is unique as compared to other states of India.
The present study entitled "Spatial and temporal variations in the development of
agriculture in Kerala" was undertaken mainly with an objective of constructing
composite indices to quantify the development of agriculture based on suitable
indicator variables for each district or region of Kerala. The significance of the
districtwise and temporal disparities in agricultural development have been studied.
The agricultural growth with respect to acreage and gross production of major crops
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is also estimated using different growth curves.
The time series data from 1970-71 to 1997-98 collected from State Planning
Board and Directorate of Economics and Statistics, Government of Kerala,
Trivandrum were used for the study. As all the districts were not present before
1985-86 state was divided into several regions. Districts wise analysis was carried
out from 1985-86 to 1997-98, whereas region wise analysis was carried out from
1970-71 to 1997-98.
For measuring the diversification level of districts or regions five indices viz.,
Herfindahl Index, Entropy Index, Modified Entropy Index, Composite Entropy Index
and Ogive Index were computed. All the quantitative indices were constructed by
using the total cropped area of seven major crops of Kerala. It was found that in most
of the periods the diversification in cropping pattern was mainly towards plantation
crops. The most diversified district was Kollam, where the cropping pattern had
equal importance to all the major crops. Based on the real situation, out of the five
measures of diversification Composite Entropy Index was found to be better suited.
It was also noticed that as time progressed the diversification level among the
districts or regions decreased.

The Compound growth rates of both production and acreage were computed
and it was found that rubber recorded the highest C.G.R. The food crops viz., rice
and tapioca showed negative C.G.R whereas cash crops viz., coconut and pepper
showed positive C.G.R for both production and acreage.
Productivity index were constructed for each district taking into consideration
the variety of crops and their relative importance in a particular district. The results
revealed that different districts behaved differently with respect to the rate of growth
of productivity.
Development is a multidimensional process, so instead of analysing a single
variable, composite index or development index for different districts or regions
were computed by using several indicators, which contributed to the development of
agriculture. In the present study three methods were used to compute the
development index based on seven indicators.
In the first approach i.e. Taxonomic approach during 1985-86, 1990-91 and
1995-96 Emakulam occupied the first place in agriculture development. However,
Wayanad and Kasargode were the two least agriculturally developed districts during
the above said periods. It was also observed that there was hardly any change in the
level of development of agriculture over different periods of study.
In Taxonomic approach each variable was considered to have equal
contribution towards the development of agriculture. However, it is unlikely to
happen so. With this fact, the Taxonomic approach was modified in Modified
Taxonomic approach by giving separate weightage to the indicators based on the
score given by experts. In the present study separate weightage did not have any
significant impact on the classification of districts or regions on their agricultural
development status. Obviously the selected variables might be highly correlated.


Characteristics in biological experiment are highly correlated. In the present
study Principal Component analysis was used to overcome this problem. The first
component of both district wise and region wise analysis contributed around 99.5 per
cent of total variation. Therefore, without loosing any information supplied by the
seven variables, the first component score was taken as the composite index of
development. Hence in the present context Principal Component analysis could be
considered as the best method, as no approximation is involved. It could be
considered as a more comprehensive method.
The Potential targets for the under developed districts or regions are also
estimated to assess the position of those districts or regions compared to the model
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districts or regions. Accordingly suitable development programmes can be launched
or special care can be taken to allocate resources optimally on per capita basis to
reduce spatial disparities in development.