International Journal of Farm Sciences
Open Access
  • Year: 2023
  • Volume: 13
  • Issue: 1

Intra-regional disparities in Kerala’s agricultural development

  • Author:
  • BJ Anjana Krishna1,*, KC Ayyoob1, M Krishnadas2, M Sajitha Vijayan1, Dayana David1
  • Total Page Count: 8
  • Published Online: Apr 18, 2023
  • Page Number: 63 to 70

1Department of Agricultural Statistics, Kerala Agricultural University, Vellanikkara, Thrissur, 680656, Kerala, India

2Department of Agricultural Economics, Kerala Agricultural University, Vellanikkara, Thrissur, 680656, Kerala, India

*Email for correspondence: anjanakrishna.lekshmi@gmail.com

Online Published on 18 April, 2023.

Abstract

Disparities in agricultural development in all the fourteen districts of Kerala were studied for the period 2015 to 2020. The standardised developmental indicators were used to find out the overall composite index. The districts were classified into homogenous groups based on the composite indices. For identifying the most contributing variables in agricultural development, principal component regression was carried out. A biplot was employed to identify major contributing variables. District Palakkad with a composite index of 0.617 was ranked first and Pathanamthitta with the highest composite index of 0.877 received the least rank. The variables such as the area under pepper, productivity of rubber, the productivity of rice, area under rubber, productivity of tubers and fertilizer consumption contributed higher to the first principal component. In contrast, the variables area under paddy, net sown area and area under irrigation contributed more to the second principal component. A district-level intervention for the improvement and increase in these indicators can help the districts to progress their position in the development of the agricultural sector of that particular district and, hence, the overall socio-economic development of the district and the state.

Keywords

Composite index, Developmental indicators, Principal component, Regression, Contributing variables