Assessment of the constraints in PHM and determinants of losses in grapes (Vitis vinifera): An ordered logistic regression analysis


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Authors

  • SUBHASHREE SAHU ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India
  • J P SHARMA Sher-e-Kashmir University of Agricultural Sciences and Technology, Jammu, Jammu and Kashmir
  • R R BURMAN ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India
  • RESHMA GILLS Central Marine Fisheries Research Institute, Kochi, Kerela
  • S K SINHA Central Marine Fisheries Research Institute, Kochi, Kerela

https://doi.org/10.56093/ijas.v92i9.118975

Keywords:

Constraints, Determinants, Marketing, Ordered logistic regression, Post-harvest losses

Abstract

Grape (Vitis vinifera L.) is one of the major fruit crops of India and the largest grape producing states are Maharashtra followed by Karnataka, Tamil Nadu, Mizoram and Andhra Pradesh. The glaring inefficiencies in the supply chain of grapes along with its perishable nature leads to huge losses. The study identified farm size, knowledge level, experience, training received, timely labour availability and distance from farm to market as significant determinants of the losses. Though a lot of research and extension has been done on the technologies for improved production and loss reduction, the adoption of the recommendations by the farmers has been found to be differential. Thus, we need to isolate the factors which hinder the adoption of such recommendations. The present study (2019–21) identified the major constraints as the high initial investment in infrastructure, lack of market intelligence system, unavailability of skilled labour, unavailability of timely chemicals and equipments and thus, chalks out the future strategy accordingly to minimise the losses.

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Submitted

2021-12-12

Published

2022-09-08

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How to Cite

SAHU, S., SHARMA, J. P., BURMAN, R. R., GILLS, R., & SINHA, S. K. (2022). Assessment of the constraints in PHM and determinants of losses in grapes (Vitis vinifera): An ordered logistic regression analysis. The Indian Journal of Agricultural Sciences, 92(9), 1129–1133. https://doi.org/10.56093/ijas.v92i9.118975
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