M Sc

Agriculture is the largest sector of economic activity in Kerala and has a crucial role to
play in economic development by providing food and raw materials, employment to a
very large proportion of the population, capital for its own development and surpluses
for economic development. In this context a study on the analysis of the trends
regarding the data for a period from 1960-‘61 to 2019-‘20 on area under cultivation,
production and productivity of paddy in Kerala has great importance. An empirical
study was also attempted to identify the vital factors leading to the enhancement of net
income of paddy farmers using Structural equation modelling on the primary data
collected from 150 registered paddy farmers of Ollukkara block of Thrissur district.
The trend analysis of area, production and productivity of paddy in Kerala for the period
from 1960-‘61 to 2019-‘20 pertaining to autumn, winter and summer seasons revealed
that area under paddy and production had a declining trend in autumn and winter
seasons whereas an increasing trend in the case of summer paddy. Paddy productivity
has an increasing trend in all the seasons.
Employing Bai and Perron (1998) methodology, breaks in the time series of area,
production and productivity of paddy in Kerala for different seasons were identified
and were used to explore volatilities of paddy production in different phases.
Compound Annual Growth Rate (CAGR) and instability indices were computed with
respect to each break points of the trend and used to explain the growth pattern of the
variables over the study period owing to the fact that paddy is one of the most essential
food crops in Kerala. In the recent past, area under cultivation of paddy had been
declining due to several factors including the adoption of non-agricultural food crops
like rubber and coconut which would provide better returns to farmers. CAGR on area,
production and productivity showed a declining trend upto 2008-‘09. Area under
autumn paddy was the most affected variable resulted in negative growth rates in all
phases. In contrast, the growth rate was positive for productivity in almost all phases of
different seasons. However, in the subsequent year to the enactment of the Kerala paddy
conservation and wetland act, the area and production of paddy for all the three seasons
gradually started increasing depicting a positive impact of the act on paddy cultivation
in the state. The growth instability was maximum for summer paddy production.
Time series modelling and forecasting analysis identified Browns’ exponential
smoothing model as the best with significantly high value R2 = 0.99 for area under
paddy in autumn, ARIMA (0,1,0) with R2 = 0.98 in winter and Simple exponential
smoothing model resulted in an R
2 = 0.93 in summer. Coming to paddy production in
autumn, Browns’ exponential smoothing model resulted in an R
2 = 0.95 and Simple
exponential smoothing model seemed to be the best for winter and summer season with
R
2 = 0.87 and R
2 = 0.60 respectively. Holts’ exponential smoothing model was found
as the best with R
2 = 0.87 to predict paddy productivity in autumn and summer and
Browns’ exponential smoothing model with R
2 = 0.87 for winter paddy productivity.
The secondary data collected from the year 1996-‘97 to 2018-‘19 on area, production,
productivity and price of paddy from the official website (DES), Kerala were made use
of to forecast the yearly change in paddy production by fitting a regression of change
in production on yearly change in cultivated area, yearly change in productivity, yearly
change in price, and the interaction of yearly change in price and area. The regression
equation resulted in an adjusted R
2
of 0.73 and yearly change in area and productivity
were the significant regressors.
An empirical analysis was conducted using a sample of 150 registered paddy farmers
from Ollukkara block of Thrissur district to determine the factors considered by farmers
to influence their paddy production and, ultimately leading to their net income. Several
linear regression equations could be constructed simultaneously from a path analysis
using structural equation modelling, leading to prediction equations for paddy
production and net income. The final model iterated, resulted in goodness of fit
measures viz; comparative fit index = 0.90 and Tucker Lewis index = 0.90 and RMSEA
= 0.08 emphasising the potential of SEM in plant science studies as powerful as in
social science. Finally the constraints faced by the farmers in paddy farming were
ranked according to their severity and coefficient of concordance was computed as w=
0.423 which was significant at 1 per cent level showing strong agreement among the
farmers to rank the constraints as “financial, labour management, pest, disease and
animal attack, marketing and lack of knowledge in paddy farming”. However in spite
of all these constraints farmers are now attracted towards paddy farming because of the
enriched net returns from it.