社会人口因素和处理做法对Kakamega县养殖罗非鱼价值链收获后鱼类损失的影响

IF 1.1 Q3 FISHERIES
James Adero Obar, Joseph Wafula Matofari, John Masani Nduko
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引用次数: 0

摘要

收获后鱼类损失严重影响了肯尼亚的水产养殖部门,破坏了粮食安全和经济增长。本研究调查了Kakamega县养殖罗非鱼价值链中影响PHFL的社会人口因素和处理方法。通过调查和观察,从六个副县的94个价值链参与者收集了跨多个节点的数据,包括农业、加工和零售阶段。结果显示,64%的参与者是女性,40.3%的人完成了高中学业,只有26.6%的人持有有效的食品加工证书。家庭规模大多在4到9人之间,在价值链上的经验各不相同,33%的人有5到10年的经验,17%的人有21年以上的经验。PHFL在各节点间均很显著,Butere的农场门口损失最高(32%),Likuyani的农场门口损失最低(8.42%)。批发和零售业的亏损也很明显,其中Mumias East的整体亏损最高。回归分析确定了PHFL的关键预测因素,包括卫生条件差、缺乏培训、水质不合格和冷却措施,解释了PHFL方差的87.2%。经验与PHFL呈负相关(r = - 0.354, p <;0.01),说明经验越丰富的个体损失越小。卡方检验显示,PHFL与家庭规模、职业、培训和卫生等因素之间存在显著关联,但与性别、教育或年龄无关。研究结果强调需要有针对性的干预措施,包括培训计划、基础设施改善和遵守食品安全标准,以尽量减少PHFL,改善鱼类质量并加强Kakamega县的粮食和营养安全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Impact of Socio-Demographic Factors and Handling Practices on Post-Harvest Fish Losses in Kakamega County's Cultured Tilapia Value Chain

Impact of Socio-Demographic Factors and Handling Practices on Post-Harvest Fish Losses in Kakamega County's Cultured Tilapia Value Chain

Post-harvest fish losses (PHFL) significantly impact the aquaculture sector in Kenya, undermining food security and economic growth. This study investigated socio-demographic factors and handling practices influencing PHFL in Kakamega County's cultured tilapia value chain. Data were collected from 94 value chain actors operating across multiple nodes, including farming, processing and retail stages from six sub-counties using surveys and observations. Results revealed that 64% of participants were female, and 40.3% had completed high school, with only 26.6% holding valid food handler certificates. Household sizes mostly ranged from 4 to 9 members, and experience in the value chain varied, with 33% having 5–10 years of experience and 17% had over 21 years of experience. PHFL were significant across nodes, with farm gate losses highest in Butere (32%) and lowest in Likuyani (8.42%). Wholesale and retail losses were also notable, with Mumias East showing the highest overall losses. Regression analysis identified key predictors of PHFL, including poor sanitary conditions, lack of training, inadequate water quality and cooling practices, explaining 87.2% of the variance in PHFL. Experience negatively correlated with PHFL (r = −0.354, p < 0.01), indicating that more experienced individuals incurred lower losses. Chi-square tests showed significant associations between PHFL and factors like household size, occupation, training and sanitation but not gender, education or age. The findings highlight the need for targeted interventions, including training programmes, infrastructure improvements and adherence to food safety standards, to minimise PHFL, improve fish quality and enhance food and nutrition security in Kakamega County.

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