TY  - BOOK
AU  - Allahad Mishra 
AU  - Ajitha T K (Guide)
TI  - Spatial and temporal variations in the development of agriculture in Kerala
U1  - 640 
PY  - 2002///
CY  - Vellanikkara
PB  - Department of Agricultural Statistics, College of Horticulture
N2  - 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 
• 
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 
• 
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. 


UR  - https://krishikosh.egranth.ac.in/handle/1/5810156521
ER  -