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Book (stand-alone)Technical reportReport on pre- and post-harvest crop losses pilot survey (2021–2022) 2023
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No results found.The 2021–2022 (2014 Ethiopian calendar) pre- and post-harvest loss pilot survey aimed to produce data on the magnitude of pre-harvest damages and post-harvest losses of maize, wheat, faba beans, and haricot bean crops across the post-harvest value chain. It covered the three regions of Ethiopia, namely Amhara, Oromia and Southern Nations and Nationalities regions. -
ArticleJournal articleCombining farm and household surveys with modelling approaches to improve post-harvest loss estimates and reduce data collection costs 2022
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No results found.While there is growing awareness of the issue of food losses at the political level, official post-harvest loss data for informing policymaking and reporting on SDG Indicator 12.3.1. (a) Food Loss Index is scarce. Representative sample-based surveys are necessary to obtain information on on-farm losses at the country level, but due to the issue’s complexity, a loss module covering several key questions is needed. One main strategy proposed by the 50x2030 Initiative for optimizing data collection is sub-sampling for some of the survey modules. This paper examines whether modelling approaches can be combined with sub-sampling to improve harvest and post-harvest loss estimates and allow for further sample and cost reduction. The paper first presents the loss models generated on four selected surveys conducted in Malawi, Zimbabwe, and Nigeria, which were built using the Classification and Regression Tree (CART) method. The performance of each model is assessed for different sizes of sub-samples to improve the sample-based estimates, either by model-based estimates or by model-based imputation. The research concludes that the model-based estimates improve the loss estimates of the sub-samples due to post-stratification implied in the CART method, whereby they can constitute a cost-effective complement to sub-sampling strategies, while model-based imputations should only be used on a reduced number of missing observations. The models perform best when the survey invests in obtaining more detailed on-farm loss data and considers some key variables identified as relevant for on-farm loss models. Sub-sampling allows for investment in more detailed questionnaires and some considerations are derived for its design. -
ArticleJournal articleExtent of pre-harvest and post-harvest losses and their causes: identifying critical loss points in the dried bean supply chain of the school meals program in Kajiado and Kitui counties of Kenya 2025
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No results found.The extent of pre- and post-harvest losses in supply chains linked to the home-grown school meals program (HGSMP) is not documented. This study sought to fill this gap and determine critical loss points along the dried bean supply chain of the HGSMP. The study was conducted in Kajiado and Kitui Counties. Secondary and primary data were collected for this study. Primary data was collected from all the schools implementing the HGSMP and all other supply chain actors linked to the programme within the two counties through interviews and direct measurement of the losses (load-tracking). Data was analysed using the FAO case study methodology. Producers reported quantitative losses of about 18.4% and 6.6% in Kitui and Kajiado Counties, respectively. Traders estimated quantitative losses at 5.8% and 12.6% in Kajiado and Kitui, respectively. The study revealed that the storage stage is a critical loss point for both producers and traders. Promotion of awareness and appropriate technologies and practices for storage and post-harvest handling of food commodities procured for school meals can contribute to reducing losses. Capacity building of supply chain actors on proper pre-harvest agricultural practices and post-harvest management is also essential for the reduction of pre- and post-harvest losses.
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