Version 1
: Received: 29 August 2024 / Approved: 3 September 2024 / Online: 5 September 2024 (12:35:49 CEST)
How to cite:
Wanjala Omondi, S.; Kidali, J.; Kamau, E.; M. Murithi, F. Critical Control Points for Reducing Post‐Harvest Losses along the Milk Value Chain: Insights from Nandi and Kakamega Counties, Kenya. Preprints2024, 2024090291. https://doi.org/10.20944/preprints202409.0291.v1
Wanjala Omondi, S.; Kidali, J.; Kamau, E.; M. Murithi, F. Critical Control Points for Reducing Post‐Harvest Losses along the Milk Value Chain: Insights from Nandi and Kakamega Counties, Kenya. Preprints 2024, 2024090291. https://doi.org/10.20944/preprints202409.0291.v1
Wanjala Omondi, S.; Kidali, J.; Kamau, E.; M. Murithi, F. Critical Control Points for Reducing Post‐Harvest Losses along the Milk Value Chain: Insights from Nandi and Kakamega Counties, Kenya. Preprints2024, 2024090291. https://doi.org/10.20944/preprints202409.0291.v1
APA Style
Wanjala Omondi, S., Kidali, J., Kamau, E., & M. Murithi, F. (2024). Critical Control Points for Reducing Post‐Harvest Losses along the Milk Value Chain: Insights from Nandi and Kakamega Counties, Kenya. Preprints. https://doi.org/10.20944/preprints202409.0291.v1
Chicago/Turabian Style
Wanjala Omondi, S., Elias Kamau and Festus M. Murithi. 2024 "Critical Control Points for Reducing Post‐Harvest Losses along the Milk Value Chain: Insights from Nandi and Kakamega Counties, Kenya" Preprints. https://doi.org/10.20944/preprints202409.0291.v1
Abstract
This study sought to investigate the problem of losses along the milk value chain in Nandi and Kakamega Counties in Kenya. Using semi structured questionnaire, quantitative data were collected from different stages of the value chain consisting of 400 dairy farmers proportionally and randomly selected in Chesumei and Ikolomani sub counties; 15 cooperatives, 40 transporters, 20 milk bars, 17 supermarkets. Additionally Key informant interviews and focus group discussions we held with extension staff and farmers, respectively.The results showed losses occurred at all stages of the value chain in Nandi and Kakamega Counties with the highest losses at Cooperative (50.6%), collection centres (33.7%) and milk bars (7.5%). Annual milk loss was estimated to be 260,000 litres and 20,000 litres in Nandi and Kakamega counties respectively. The economic value of losses per year was estimated at KES 13 million (USD 100 000). Significant factors (P<0.001) influencing milk losses at farm level were: type of cow housing, level of training of milker, mixing morning and evening milk and Lack of cooling facilities. In cooperatives, poor roads, poor preservation of milk and mastitis influenced post harvest losses (PHL) causing milk rejection due to spoilages; while transporters attributed PHL to storage during transportation. Inadequate storage facilities and milk handling practices influenced PHL in milk bars while supermarkets attributed PHL to poor packaging materials and short shelf life. The most important causes of milk PHL along the chain were spoilages, spillages, adulteration and physical dirt. Key interventions to minimize PHL should target improvement of rural road infrastructure connecting farmers to markets; milk storage facilities and capacity building on milk handling practices for all actors. A sustainable PHL reduction should entail policy dialogue on milk payment system based on quality rather than quantity.
Keywords
Post harvest losses, milk value chain, farmers, cooperatives, Kenya
Subject
Social Sciences, Behavior Sciences
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
