Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Research Articles

Vol. 12 No. sp1 (2025): Recent Advances in Agriculture by Young Minds - II

Identifying key determinants of pulse crop productivity in Jammu region using principal component analysis

DOI
https://doi.org/10.14719/pst.6238
Submitted
21 November 2024
Published
14-08-2025 — Updated on 28-08-2025
Versions

Abstract

This study examines the factors influencing agricultural productivity, with a specific focus on pulse cultivation, in the purposively selected districts of Kathua, Doda, Kishtwar and Udhampur in the Jammu region. Primary data were collected from 240 farmers using a multistage random sampling technique. To identify the underlying determinants of productivity, Principal Component Analysis (PCA) was employed. The PCA results extracted four principal components in each district, collectively accounting for 71 % to 81 % of the total variance in the dataset. The analysis identified that land characteristics, income diversification and farmer education were the most significant contributors to agricultural performance. Land fragmentation and ownership emerged as critical factors in Doda and Kishtwar, whereas income sources and educational qualifications of farmers were more influential in Kathua and Udhampur. These findings highlight the importance of region-specific policy interventions, particularly those aimed at improving land management, promoting income diversification and enhancing farmer education, to effectively boost pulse productivity and overall agricultural sustainability in the region.

References

  1. 1. Jadhav V, Swamy NM, Gracy CP. Supply-demand gap analysis and projection for major pulses in India. Econ Aff. 2018;63(1):277‒85. https://doi.org/10.30954/0424-2513.2018.00150.34
  2. 2. Anonymous. Directorate of pulses development, pulses in India: Retrospect and prospects. Annual Report: 2022-2023; 2024 Available from: https://dpd.gov.in/AbtRep.html
  3. 3. FAO. World Food and Agriculture - Statistical yearbook 2020. Rome; 2020 Available from: https://doi.org/10.4060/cb1329en
  4. 4. NAAS. Sustaining the pulses revolution in India: Technological and policy measures. Policy paper No. 116, National Academy of Agricultural Sciences, New Delhi. 2022; 24p. Available from: https://naas.org.in/Policy%20Papers/policy%20116.pdf
  5. 5. Bhat S, Aditya KS, Kumari B, Acharya KK, Sendhil R. Pulses production, trade and policy imperatives: A global perspective. In: Meena RS, Kumar S, editors. Advances in legumes for sustainable intensification. Academic Press; 2022. p. 39‒656 https://doi.org/10.1016/B978-0-323-85797-0.00018-5
  6. 6. Singh G. Status and digital documentation of pulse crops in the temperate area of Jammu region. PhD [dissertation]. Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu; 2021 Available from: https://krishikosh.egranth.ac.in/items/258a6035-5c3c-429f-8a11-ff4d709f009b
  7. 7. Tharanathan RN, Mahadevamma S. Grain legumes- a boon to human nutrition. Trends in Food Sci Technol. 2021;14:507‒18. https://doi.org/10.1016/j.tifs.2003.07.002
  8. 8. Singh A. An overview of export and import of pulses in India. Int J Eng Manag Res. 2021;11(4):281–83. https://doi.org/10.31033/ijemr.11.4.36
  9. 9. Anonymous. Directorate of Economics and Statistics, J&K Government; 2015 Available from: https://ecostatjk.nic.in/
  10. 10. Balwan WK, Saba N, Singh N, Rasool N. Solid waste management: first report on garbage problem in Doda region of Jammu and Kashmir, India. Intern J Engineer Appli Sci and Technol. 2020;5(7):157‒73. https://doi.org/10.33564/IJEAST.2020.v05i07.026
  11. 11. Ganaie MI, Wani MA, Dev A, Mayer IA. Pesticide exposure of farm community causing illness symptoms in upper Jhelum Basin of Kashmir Himalaya, India. Environ Dev Sustain. 2022;24(12):13771‒85. https://doi.org/10.1007/s10668-021-02012-9
  12. 12. Rachappanavar V, Sharma JK, Pandey H, Indrakumar K. Scope for organic farming in Himalayan region. In: Meena ML, editor. Research trends in horticulture sciences. Akinik Publications; 2019. p. 71‒89
  13. 13. Khan BA. Demography of Jammu and Kashmir in historical perspective. Asian Rev Social Sci. 2018;7(3):143‒53. https://doi.org/10.51983/arss-2018.7.3.1453
  14. 14. Vaid A, Mahajan V, Sharma PK, Gupta S, Ajrawat B, Jamwal A, et al. Impact of varietal demonstrations of wheat HD-2967 in Kathua. Maharashtra J Agric Econ. 2017;20(2):115–16.
  15. 15. Sharma S, Singh SP, Singh M, Kumar S. Socio-economic status of maize growers in Udhampur district of Jammu region. Intern J Theoretical and Appli Sci. 2022;14(2):23‒25.
  16. 16. Kumar B. Common Property Resources (CPR) and their sustainable use among the Gaddi scheduled tribe of Jammu and Kashmir. Res J Soc Sci. 2018;9(12):28‒34.
  17. 17. Raina A, Sharma V. Problems and prospects of Himalayan farmers and farming: a case study of district Kishtwar, Jammu and Kashmir. Regional Economic Development Res. 2021;2(1):82‒95. https://doi.org/10.37256/redr.212021776
  18. 18. Dutta C, Borah D, Das P. Farmers’ level of satisfaction on Agricultural Technology Information Centre of AAU, Jorhat, Assam. Indian Res J Ext Edu. 2021;21(4):85‒91.
  19. 19. Parkash S, Peshin R. Growers’ knowledge of improved maize production technologies in Jammu Region of J&K. Indian J Ext Edu. 2020;56(3):41‒47.
  20. 20. Hett C, Nanhthavong V, Hanephom S, Phommachanh A, Sidavong B, Phouangphet K, et al. Land leases and concessions in the Lao PDR: A characterization of investments in land and their impacts. Bern: Centre for Development and Environment (CDE), University of Bern, Switzerland. Bern Open Publishing; 2020. p. 150 https://doi.org/10.7892/boris.133115
  21. 21. Jolliffe IT, Cadima J. Principal component analysis: A review and recent developments. Phil Trans R Soc A. 2016;374(2065):20150202. https://doi.org/10.1098/rsta.2015.0202
  22. 22. Demsar U, Harris P, Brunsdon C, Fotheringham AS, McLoone S. Principal component analysis on spatial data: An overview. Ann Assoc Am Geogr. 2012;103(1):106‒28. https://doi.org/10.1080/00045608.2012.689236
  23. 23. Kyriazos T, Poga M. Dealing with multicollinearity in factor analysis: the problem, detections and solutions. Open J Stat. 2023;13(3):404‒24. https://doi.org/10.4236/ojs.2023.133020
  24. 24. Pareek J, Jacob J. Data compression and visualization using PCA and T-SNE. In: Goar V, Kuri M, Kumar R, Senjyu T, editors. Advances in information communication technology and computing: Proceedings of AICTC 2019; Springer Singapore; 2021. p. 327‒37 https://doi.org/10.1007/978-981-15-5421-6_34
  25. 25. Shivagangavva PD, Reddy BS. Factors determining supply of pulses in India. Ind J Economics and Development. 2016;4(6):1‒6.
  26. 26. Shibani, Mehta G, Pathania S. Determinants of crop diversification: A case study of farm households in Himachal Pradesh. Ind J Economics and Development. 2023;19(4):847‒54. https://doi.org/10.35716/IJED-23326
  27. 27. Verma DK, Singh H, Meena GL, Suman J, Sachan S. Factors affecting production of important pulse crops in Rajasthan: a cobb Douglas analysis. Legume Res- An Intern J. 2023;46(3):364‒67.
  28. 28. Gebiso T, Ketema M, Shumetie A, Leggesse G. Crop diversification level and its determinants in Ethiopia. Cogent Food and Agric. 2023;9(2):2278924. https://doi.org/10.1080/23311932.2023.2278924

Downloads

Download data is not yet available.