TO STUDY THE SHIFT IN CROPPING PATTERN OF DIFFERENT AGRO-CLIMATIC ZONES IN ANDHRA PRADESH - A STATISTICAL ANALYSIS
Loading...
Date
2024-01-10
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Acharya N G Ranga Agricultural University
Abstract
Diversification in agriculture refers to change in cropping pattern or the
expansion of non-farming activities such as poultry farming, aquaculture, animal
husbandry, etc. Andhra Pradesh is endowed with varied agro-ecological, agro-climatic,
bio-diversity, soil, climatic and weather conditions, comprising of six agroclimatic
zones. The data is collected on area, production and productivity of major agricultural
crops during the period 2001-2020. The entire twenty years data is divided into two
periods i.e., first period 2001-2010 and the second period 2011-2020.
To analyze the extent of shift in cropping pattern across six agro-climatic zones
of Andhra Pradesh, various statistical indices such as Herfindahl Index, Simpson Index,
Entropy Index, Index of Maximum Proportion, Modified Entropy Index, Composite
Entropy Index and Ogive Index were calculated for all the zones separately for the two
periods 2001-2010 and 2011-2020. The results revealed that Krishna zone, Scarce
Rainfall zone and Southern zone have experienced considerable cropping shift in both
the periods. North coastal zone has experienced moderate crop shift for both the
periods. High-Altitude zone has considerable cropping shift for first period and a
moderate cropping shift for the second period of study. Godavari zone has shown crop
specialization. To study the direction of changes in cropping pattern, Markov Chain
Analysis was conducted for the zones with cropping shift.
To analyze the factors influencing the shift in cropping pattern, 11 relatable
variables were considered for the study. Principal Component Analysis (PCA) was
carried to reduce the dimensionality of the factors influencing the shift. The Principal
components extracted maximum variability are considered as independent variables and
Simpson index values are considered as dependent variable in regression analysis.
Principal Component regression (PCR) was carried out for both the periods 2001-2010
and 2011-2020.
The Cluster analysis was carried out based on area, production and productivity
of different agricultural crops for the periods 2001-2010 and 2011-2020. For forming
xiv
clusters based on area, production and productivity of major agricultural crops,
Hierarchical clustering with Ward’s minimum variance method was computed. The
Dendrogram graphically represents the results of Hierarchal cluster analysis. The cluster
formed based on area, production and productivity indicates that those crops have
similarity across all the zones